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Journal articles on the topic 'VGG16 MODEL'

Author: Grafiati

Published: 11 September 2023

Last updated: 31 July 2025

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1

Hindarto, Djarot, Nihayah Afarini, and Endah T. Esthi H. "Comparison Efficacy of VGG16 and VGG19 Insect Classification Models." JIKO (Jurnal Informatika dan Komputer) 6, no. 3 (2023): 189–95. http://dx.doi.org/10.33387/jiko.v6i3.7008.

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This study compares two popular deep-learning models, VGG16 and VGG19, for insect classification. This study aims to evaluate insect detection architectures to automate insect identification. We use a large, heterogeneous dataset of insect species, including common pests and beneficial insects, and their images to achieve this goal. The dataset was used to re-adjust the VGG16 and VGG19 models and analyze their classification performance. With an average improvement of 1,8%, VGG19 outperforms VGG16 in insect classification accuracy. VGG19 is more robust because it can handle complex traits and

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Handayani, Vitria Wuri, Ahmad Yudianto, Mieke Sylvia M.A.R, Riries Rulaningtyas, and Muhammad Rasyad Caesarardhi. "Classification of Indonesian adult forensic gender using cephalometric radiography with VGG16 and VGG19: a Preliminary research." Acta Odontologica Scandinavica 83 (May 21, 2024): 308–16. http://dx.doi.org/10.2340/aos.v83.40476.

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Background: The use of cephalometric pictures in dental radiology is widely acknowledged as a dependable technique for determining the gender of an individual. The Visual Geometry Group 16 (VGG16) and Visual Geometry Group 19 (VGG19) algorithms have been proven to be effective in image classification. Objectives: To acknowledge the importance of comprehending the complex procedures associated with the generation and adjustment of inputs in order to obtain precise outcomes using the VGG16 and VGG19 algorithms. Material and Method: The current work utilised a dataset including 274 cephalometric

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Jin, Yancheng. "Optimizing Intracranial Hemorrhage Detection: A Comparative Study of VGG Deep Learning Architectures with Transfer Learning on CT Brain scans." Highlights in Science, Engineering and Technology 85 (March 13, 2024): 1056–64. http://dx.doi.org/10.54097/mb06y339.

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Intracranial Hemorrhage (ICH) is a life-threatening condition that requires rapid and accurate diagnosis. This study applied Visual Geometry Group (VGG) deep learning model to binary classification of cerebral hemorrhage in Computerized Tomography (CT) brain scans, thereby improving the diagnostic process. Using a Kaggle dataset containing 6703 images, this study investigated the validity of VGG11, VGG13, VGG16, and VGG19 architectures, using transfer learning of ImageNet pre-trained weights to improve model performance. This study showed that VGG13 with transfer learning outperformed other mo

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Sukegawa, Shintaro, Kazumasa Yoshii, Takeshi Hara, et al. "Deep Neural Networks for Dental Implant System Classification." Biomolecules 10, no. 7 (2020): 984. http://dx.doi.org/10.3390/biom10070984.

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In this study, we used panoramic X-ray images to classify and clarify the accuracy of different dental implant brands via deep convolutional neural networks (CNNs) with transfer-learning strategies. For objective labeling, 8859 implant images of 11 implant systems were used from digital panoramic radiographs obtained from patients who underwent dental implant treatment at Kagawa Prefectural Central Hospital, Japan, between 2005 and 2019. Five deep CNN models (specifically, a basic CNN with three convolutional layers, VGG16 and VGG19 transfer-learning models, and finely tuned VGG16 and VGG19) w

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Verma, Aditya, and Disha Wankhede. "Robust Brain Tumour Classification: Comparative Deep Learning Analysis with Ensemble Modelling." EPJ Web of Conferences 328 (2025): 01033. https://doi.org/10.1051/epjconf/202532801033.

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Accurate detection of brain tumours from MRI scans is essential for early diagnosis and treatment. Traditional approaches, including manual analysis by radiologists and classical machine learning methods relying on handcrafted features, often lack consistency and high accuracy. This study explores VGG16, VGG19, DenseNet121, ResNet50, Ensemble model (VGG16 + DenseNet121), MobileNetV2, and NASNet for automated brain tumour detection. Using the Brain Tumour Classification (MRI) dataset, VGG16 and DenseNet121 achieved the highest accuracy of 94.08%, demonstrating the effectiveness of transfer lear

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Kumar, Vijay, Anis Zarrad, Rahul Gupta, and Omar Cheikhrouhou. "COV-DLS: Prediction of COVID-19 from X-Rays Using Enhanced Deep Transfer Learning Techniques." Journal of Healthcare Engineering 2022 (April 11, 2022): 1–13. http://dx.doi.org/10.1155/2022/6216273.

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In this paper, modifications in neoteric architectures such as VGG16, VGG19, ResNet50, and InceptionV3 are proposed for the classification of COVID-19 using chest X-rays. The proposed architectures termed “COV-DLS” consist of two phases: heading model construction and classification. The heading model construction phase utilizes four modified deep learning architectures, namely Modified-VGG16, Modified-VGG19, Modified-ResNet50, and Modified-InceptionV3. An attempt is made to modify these neoteric architectures by incorporating the average pooling and dense layers. The dropout layer is also add

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Utami, Nengah Widya, I Nyoman Purnama, and I Putu Restu Prajna. "KLASIFIKASI TANAMAN UPAKARA ADAT HINDU DI KEBUN RAYA EKA KARYA BALI MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK." Jurnal Informatika Teknologi dan Sains (Jinteks) 5, no. 4 (2023): 671–78. http://dx.doi.org/10.51401/jinteks.v5i4.3416.

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Penelitian ini bertujuan untuk mengimplementasikan klasifikasi semantik secara otomatis yang dapat membedakan jenis tanaman sebagai alternatif pengenalan objek berdasarkan metode deep learning menggunakan Convolutional Neural Network (CNN). Metode CNN merupakan salah satu metode deep learning yang mampu melakukan proses pembelajaran mandiri untuk pengenalan objek, ekstraksi objek dan klasifikasi serta dapat diterapkan pada citra resolusi tinggi yang memiliki model distribusi nonparametrik. Pada penelitian ini, diterapkan algoritma CNN untuk membedakan jenis tanaman upakara Hindu di Bali dengan

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Reddy C, Kishor Kumar, Aarti Rangarajan, Deepti Rangarajan, Mohammed Shuaib, Fathe Jeribi, and Shadab Alam. "A Transfer Learning Approach: Early Prediction of Alzheimer’s Disease on US Healthy Aging Dataset." Mathematics 12, no. 14 (2024): 2204. http://dx.doi.org/10.3390/math12142204.

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Alzheimer’s disease (AD) is a growing public health crisis, a very global health concern, and an irreversible progressive neurodegenerative disorder of the brain for which there is still no cure. Globally, it accounts for 60–80% of dementia cases, thereby raising the need for an accurate and effective early classification. The proposed work used a healthy aging dataset from the USA and focused on three transfer learning approaches: VGG16, VGG19, and Alex Net. This work leveraged how the convolutional model and pooling layers work to improve and reduce overfitting, despite challenges in trainin

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Asok, Advait K., and Lidiya Lilly Thampi. "Automated Diagnosis of Covid-19 and Pneumonia Using Transfer Learning and Custom Segmentation on Chest X-Ray Images." WSEAS TRANSACTIONS ON COMPUTER RESEARCH 12 (June 25, 2024): 328–35. http://dx.doi.org/10.37394/232018.2024.12.32.

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The key goals of this study are to discover, demonstrate, and quantify advancements in deep learning approaches for classifying healthy, pneumonia of the community-acquired and viral types, and COVID-infected lungs from X-ray images and to learn how the pre-trained models react to the training with custom segmented images. The proposed model uses the dataset pre-processed to generate unique masks and segment the lung region to train a convolutional neural network with a transfer learning model using VGG16 and VGG19 architecture. The accuracy and F1 score results for 3-way classification with c

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Lai, Ren Yu, Kim Gaik Tay, Audrey Huong, Chang Choon Chew, and Shuhaida Ismail. "Dorsal hand Vein Authentication System Using Convolution Neural Network." International Journal of Emerging Technology and Advanced Engineering 12, no. 8 (2022): 83–90. http://dx.doi.org/10.46338/ijetae0822_11.

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This study proposes using a dorsal hand vein authentication system using transfer learning from convolutional neural network models of VGG16 and VGG19. The required images were obtained from Bosphorus Hand Vein Database. Among the 100 users, the first 80 users were treated as registered users, while the remaining users as unregistered users. 960 left-hand images of the registered users were trained during the training phase. Meanwhile, 100 images, consisting of 80 registered and 20 unregistered users, were randomly selected for the authentication application testing. Our results showed that VG

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Anitha, Koneru, Nikhitha Puppala, Harshitha Mantada, Varshith Uddanti, Sri Ram Jagadish Ponnada, and Pushya Dhanekula. "Autism Detection Based on Facial Images Using VGG16 and VGG19." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 171–74. http://dx.doi.org/10.22214/ijraset.2024.58730.

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Abstract: A mental disability called autism spectrum disorder exhibits specific difficulties with verbal and nonverbal communication, interpersonal skills, and obsessive activities. Around 1% of the total populace is impacted by it, and its side effects frequently show up during the formative stages, or during the initial two years following birth. Autism can be diagnosed at any stage in once life and is said to be a "behavioural disease" because in the first two years of life symptoms usually appear. There hasn't been a strong diagnosis method, though, because there aren't any discernible var

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Ben Khalifa, Amani, Manel Mili, Mezri Maatouk, et al. "Deep Transfer Learning for Classification of Late Gadolinium Enhancement Cardiac MRI Images into Myocardial Infarction, Myocarditis, and Healthy Classes: Comparison with Subjective Visual Evaluation." Diagnostics 15, no. 2 (2025): 207. https://doi.org/10.3390/diagnostics15020207.

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Background/Objectives: To develop a computer-aided diagnosis (CAD) method for the classification of late gadolinium enhancement (LGE) cardiac MRI images into myocardial infarction (MI), myocarditis, and healthy classes using a fine-tuned VGG16 model hybridized with multi-layer perceptron (MLP) (VGG16-MLP) and assess our model’s performance in comparison to various pre-trained base models and MRI readers. Methods: This study included 361 LGE images for MI, 222 for myocarditis, and 254 for the healthy class. The left ventricle was extracted automatically using a U-net segmentation model on LGE i

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Bodavarapu, Pavan Nageswar Reddy, and P. V. V. S. Srinivas. "Facial expression recognition for low resolution images using convolutional neural networks and denoising techniques." Indian Journal of Science and Technology 14, no. 12 (2021): 971–83. http://dx.doi.org/10.17485/ijst/v14i12.14.

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Background/Objectives: There is only limited research work is going on in the field of facial expression recognition on low resolution images. Mostly, all the images in the real world will be in low resolution and might also contain noise, so this study is to design a novel convolutional neural network model (FERConvNet), which can perform better on low resolution images. Methods: We proposed a model and then compared with state-of-art models on FER2013 dataset. There is no publicly available dataset, which contains low resolution images for facial expression recognition (Anger, Sad, Disgust,

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Hei, Yunxi. "Exploring the Validity of Knockoff Nets Model Stealing Attack on Vgg16 Based on Different Models." Applied and Computational Engineering 106, no. 1 (2024): 118–24. http://dx.doi.org/10.54254/2755-2721/106/20241286.

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Model stealing attacks represented by the Knockoff Nets method steal the intellectual property of AI models by black-box querying. Model stealing attacks on a wide range of deep learning models have attracted widespread attention in recent years. However, there has not been any research on stealing attacks based on common models such as VGG16, ResNet18, AlexNet, etc., especially since the research on the validity of the attack on the VGG16 model is still insufficient. Therefore, in this paper, three types of models, VGG16, ResNet18, and AlexNet, are used as the models for stealing, and the Kno

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Shinde, Krishna K., and C. N. Kayte. "Fingerprint Recognition Based on Deep Learning Pre-Train with Our Best CNN Model for Person Identification." ECS Transactions 107, no. 1 (2022): 2209–20. http://dx.doi.org/10.1149/10701.2209ecst.

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In this article, we use pre-train VGG16, VGG19, and ResNet50 with ImageNet wights and our best CNN model to identify human fingerprint patterns. The system including pre-processing phase where the input fingerprint images first technique apply cropping and normalize for unwanted part remove of fingerprint images and normalize its dimension, second Image Enhancement for removing noise in to ridgelines, and last Canny Edge Detection technique for adjustment to smooth image with Gaussian to remove noise. Then apply one by one model on KVKR fingerprint dataset. Our best CNN model has automatically

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Athavale, Vijay Anant, Suresh Chand Gupta, Deepak Kumar, and Savita. "Human Action Recognition Using CNN-SVM Model." Advances in Science and Technology 105 (April 2021): 282–90. http://dx.doi.org/10.4028/www.scientific.net/ast.105.282.

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In this paper, a pre-trained CNN model VGG16 with the SVM classifier is presented for the HAR task. The deep features are learned via the VGG16 pre-trained CNN model. The VGG 16 network is previously used for the image classification task. We used VGG16 for the signal classification of human activity, which is recorded by the accelerometer sensor of the mobile phone. The UniMiB dataset contains the 11771 samples of the daily life activity of humans. A Smartphone records these samples through the accelerometer sensor. The features are learned via the fifth max-pooling layer of the VGG16 CNN mod

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Sandag, Green Arther, Eleonora Djamal, George Morris William Tangka, and Semmy Wellem Taju. "MRI Image Analysis for Alzheimer’s Disease Detection Using Transfer Learning: VGGNet vs. EfficientNet." CogITo Smart Journal 10, no. 2 (2024): 580–92. https://doi.org/10.31154/cogito.v10i2.836.580-592.

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This study focuses on developing an effective Alzheimer's disease (AD) classification model using MRI images and transfer learning. This research targets individuals aged 65 and above who are affected by the predominant form of dementia and utilizes an Alzheimer's Disease MRI Image dataset from Kaggle. Model selection involved options like EfficientNetB1, B3, B5, B7, VGG16, and VGG19. Two scenarios with distinct batch sizes (10 and 20) were explored in the model creation process. Evaluation, using a confusion matrix, determined that the EfficientNetB5 model yielded the highest accuracy at 99.2

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Pavan, Nageswar Reddy Bodavarapu, and V. V. S. Srinivas P. "Facial expression recognition for low resolution images using convolutional neural networks and denoising techniques." Indian Journal of Science and Technology 14, no. 12 (2021): 971–83. https://doi.org/10.17485/IJST/v14i12.14.

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Abstract <strong>Background/Objectives:</strong> There is only limited research work is going on in the field of facial expression recognition on low resolution images. Mostly, all the images in the real world will be in low resolution and might also contain noise, so this study is to design a novel convolutional neural network model (FERConvNet), which can perform better on low resolution images. <strong>Methods:</strong> We proposed a model and then compared with state-of-art models on FER2013 dataset. There is no publicly available dataset, which contains low resolution image

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Shi, Lei. "Verification and application of deep learning models in daily sports activities of teenagers." PLOS One 20, no. 6 (2025): e0322166. https://doi.org/10.1371/journal.pone.0322166.

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With the development of smart wearable devices and deep learning (DL) technology, the monitoring and analysis of daily sports activities of teenagers face new opportunities. At present, traditional CNN (Convolutional Neural Network) models are mostly used for recognition in daily sports activities. It is difficult to capture the temporal relationship between action sequences, and the ability to express important features is weak, resulting in poor recognition accuracy. This paper took badminton as the object, based on the VGG16 (Visual Geometry Group 16) model, and adopted the advantages of th

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Ko, Kyung-Kyu, and Eun-Sung Jung. "Improving Air Pollution Prediction System through Multimodal Deep Learning Model Optimization." Applied Sciences 12, no. 20 (2022): 10405. http://dx.doi.org/10.3390/app122010405.

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Many forms of air pollution increase as science and technology rapidly advance. In particular, fine dust harms the human body, causing or worsening heart and lung-related diseases. In this study, the level of fine dust in Seoul after 8 h is predicted to prevent health damage in advance. We construct a dataset by combining two modalities (i.e., numerical and image data) for accurate prediction. In addition, we propose a multimodal deep learning model combining a Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN). An LSTM AutoEncoder is chosen as a model for numerical time seri

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Fadlilatunnisa, Fanny, and Agung Mulyo Widodo. "Implementation of a Deep Learning Algorithm for The Detection of Cataract Disease Severity in Eyes." Infact: International Journal of Computers 9, no. 01 (2025): 35–43. https://doi.org/10.61179/infact.v9i01.712.

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Cataracts cloud the lens of the eye, resulting in blindness and poor vision. Approximately 18 million people are blind due to cataracts, according to the WHO. Prompt diagnosis is essential to avoid problems. This research creates a deep learning model that uses Convolutional Neural Networks (CNN) to categorise cataract severity into four groups: hypermature, normal, immature, and mature. Transfer learning is used with three CNN architectures: VGG16, VGG19, and ResNet50. Experiments from various eras were carried out using a labelled eye picture dataset for training. Using the confusion matrix,

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Susanto, Ajib, Christy Atika Sari, Eko Hari Rachmawanto, Ibnu Utomo Wahyu Mulyono, and Noorayisahbe Mohd Yaacob. "A Comparative Study of Javanese Script Classification with GoogleNet, DenseNet, ResNet, VGG16 and VGG19." Scientific Journal of Informatics 11, no. 1 (2024): 31–40. http://dx.doi.org/10.15294/sji.v11i1.47305.

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Purpose: Javanese script is a legacy of heritage or heritage in Indonesia originating from the island of Java needs to be preserved. Therefore, in this study, the classification and identification process of Javanese script letters will be carried out using the CNN method. The purpose of this research is to be able to build a model which can properly classify Javanese script, it can help in the process of recognizing letters in Javanese script easily.Methods: In this study, the Javanese script classification process has been used the transfer learning process of Convolutional Neural Network, n

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Hasan, Moh Arie, Yan Riyanto, and Dwiza Riana. "Grape leaf image disease classification using CNN-VGG16 model." Jurnal Teknologi dan Sistem Komputer 9, no. 4 (2021): 218–23. http://dx.doi.org/10.14710/jtsiskom.2021.14013.

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This study aims to classify the disease image on grape leaves using image processing. The segmentation uses the k-means clustering algorithm, the feature extraction process uses the VGG16 transfer learning technique, and the classification uses CNN. The dataset is from Kaggle of 4000 grape leaf images for four classes: leaves with black measles, leaf spot, healthy leaf, and blight. Google images of 100 pieces were also used as test data outside the dataset. The accuracy of the CNN model training is 99.50 %. The classification yields an accuracy of 97.25 % using the test data, while using test

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Suyahman, Sunardi, and Murinto. "Comparative Analysis of CNN Architectures in Siamese Networks with Test-Time Augmentation for Trademark Image Similarity Detection." Scientific Journal of Informatics 11, no. 4 (2024): 949–58. https://doi.org/10.15294/sji.v11i4.13811.

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Purpose: This study aims to enhance the detection of trademark image similarity by conducting a comparative analysis of various Convolutional Neural Network (CNN) architectures within Siamese networks, integrated with test-time augmentation techniques. Existing methods often face challenges in accurately capturing subtle visual similarities between trademarks due to limitations in feature extraction and generalization capabilities. The research seeks to identify the most effective CNN architecture for this task and to assess the impact of test-time augmentation on model performance. Methods: T

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NINGSIH, NORMA, AFIFAH DWI RAMADHANI, DJOKO SANTOSO, BINTANG DESTA RAMADHANI, and ILYASA AYASY EL GHOFIQI. "Penggunaan Metode Deep Learning untuk Pengembangan Sistem Komunikasi Cerdas bagi Penyandang Disabilitas." MIND Journal 9, no. 2 (2024): 206–19. https://doi.org/10.26760/mindjournal.v9i2.206-219.

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ABSTRAKKomunikasi merupakan kebutuhan mendasar bagi mahluk hidup agar dapat berinteraksi dengan lingkungan sekitar. Di dunia, orang-orang disabilitas khususnya tuna rungu dan sulit mendengar sebagian besar berkomunikasi menggunakan bahasa isyarat Penelitian ini mengembangkan sistem klasifikasi Bahasa Isyarat Indonesia (SIBI) menggunakan model Convolutional Neural Network (CNN) VGG16 dan VGG19 yang diintegrasikan dengan aplikasi berbasis web. Sistem ini dirancang untuk membantu komunikasi dengan penyandang disabilitas melalui klasifikasi gerakan tangan secara real-time menggunakan gambar atau w

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Abdullah, Taufik, Kana Saputra S, Hermawan Syahputra, Zulfahmi Indra, and Dinda Kartika. "Comparative Analysis of Model Architectures Using Transfer Learning Approach in Convolutional Neural Networks for Traditional Ulos Fabric Classification." Journal of Artificial Intelligence and Engineering Applications (JAIEA) 4, no. 2 (2025): 628–38. https://doi.org/10.59934/jaiea.v4i2.719.

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Ulos cloth is a traditional woven fabric of the Batak tribe in North Sumatra, valued for its aesthetic and symbolic significance in various ceremonies. The diversity of ulos motifs presents challenges in preservation due to their unique patterns and functions. This study aims to develop an accurate method for classifying ulos motifs using Transfer Learning on Convolutional Neural Network (CNN) architectures. Five popular models—VGG16, VGG19, MobileNetV3, Inception-V3, and EfficientNetV2—were evaluated on a dataset of 962 ulos images across six motif categories.The results show that Inception-V

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Shakhovska, Nataliya, and Pavlo Pukach. "Comparative Analysis of Backbone Networks for Deep Knee MRI Classification Models." Big Data and Cognitive Computing 6, no. 3 (2022): 69. http://dx.doi.org/10.3390/bdcc6030069.

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This paper focuses on different types of backbone networks for machine learning architectures which perform classification of knee Magnetic Resonance Imaging (MRI) images. This paper aims to compare different types of feature extraction networks for the same classification task, in terms of accuracy and performance. Multiple variations of machine learning models were trained based on the MRNet architecture, choosing AlexNet, ResNet, VGG-11, VGG-16, and Efficientnet as the backbone. The models were evaluated on the MRNet validation dataset, computing Area Under the Receiver Operating Characteri

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Priyatama, Adhigana, Zamah Sari, and Yufis Azhar. "Deep Learning Implementation using Convolutional Neural Network for Alzheimer’s Classification." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 7, no. 2 (2023): 310–217. http://dx.doi.org/10.29207/resti.v7i2.4707.

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Alzheimer's disease is the most common cause of dementia. Dementia refers to brain symptoms such as memory loss, difficulty thinking and problem solving and even speaking. This stage of development of neuropsychiatric symptoms is usually examined using magnetic resonance images (MRI) of the brain. The detection of Alzheimer's disease from data such as MRI using machine learning has been the subject of research in recent years. This technology has facilitated the work of medical experts and accelerated the medical process. In this study we target the classification of Alzheimer's disease images

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Singh, Tajinder Pal, Sheifali Gupta, Meenu Garg, et al. "Transfer and Deep Learning-Based Gurmukhi Handwritten Word Classification Model." Mathematical Problems in Engineering 2023 (May 3, 2023): 1–20. http://dx.doi.org/10.1155/2023/4768630.

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The world is having a vast collection of text with abandon of knowledge. However, it is a difficult and time-taking process to manually read and recognize the text written in numerous regional scripts. The task becomes more critical with Gurmukhi script due to complex structure of characters motivated from the challenges in designing an error-free and accurate classification model of Gurmukhi characters. In this paper, the author has customized the convolutional neural network model to classify handwritten Gurmukhi words. Furthermore, dataset has been prepared with 24000 handwritten Gurmukhi w

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Desai, Chitra. "Image Classification Using Transfer Learning and Deep Learning." International Journal of Engineering and Computer Science 10, no. 9 (2021): 25394–98. http://dx.doi.org/10.18535/ijecs/v10i9.4622.

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Deep learning models have demonstrated improved efficacy in image classification since the ImageNet Large Scale Visual Recognition Challenge started since 2010. Classification of images has further augmented in the field of computer vision with the dawn of transfer learning. To train a model on huge dataset demands huge computational resources and add a lot of cost to learning. Transfer learning allows to reduce on cost of learning and also help avoid reinventing the wheel. There are several pretrained models like VGG16, VGG19, ResNet50, Inceptionv3, EfficientNet etc which are widely used. Thi

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Hajam, Mohd Asif, Tasleem Arif, Akib Mohi Ud Din Khanday, and Mehdi Neshat. "An Effective Ensemble Convolutional Learning Model with Fine-Tuning for Medicinal Plant Leaf Identification." Information 14, no. 11 (2023): 618. http://dx.doi.org/10.3390/info14110618.

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Accurate and efficient medicinal plant image classification is of utmost importance as these plants produce a wide variety of bioactive compounds that offer therapeutic benefits. With a long history of medicinal plant usage, different parts of plants, such as flowers, leaves, and roots, have been recognized for their medicinal properties and are used for plant identification. However, leaf images are extensively used due to their convenient accessibility and are a major source of information. In recent years, transfer learning and fine-tuning, which use pre-trained deep convolutional networks

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Mao, Xinyu, Mingrui Xie, and Ruochen Zhu. "Performance comparison of different convolutional neural network models and optimizers for dog bread classification." Applied and Computational Engineering 16, no. 1 (2023): 99–105. http://dx.doi.org/10.54254/2755-2721/16/20230875.

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This study explores the performance of various deep learning models including ResNet152, VGG16, VGG19 and ResNet256 on the dog breed classification task. During training, observe the loss and accuracy trends. The loss gradually decreases, showing that the model is fitting the training data better. With the improvements of the capacity of the model, the accuracy trend shows a steady increase. These models converge after about 20 epochs and fluctuate little after that. The initial learning rate, adjustment factor and patience parameters play key roles in the convergence process. However, the ach

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Adithama, Stephanie Pamela, B. Yudi Dwiandiyanta, and Sevia Berliana Wiadji. "Identification of Batik in Central Java using Transfer Learning Method." Jurnal Buana Informatika 14, no. 02 (2023): 77–86. http://dx.doi.org/10.24002/jbi.v14i02.6977.

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Identification of Batik in Central Java using Transfer Learning Method. Batik was recognized as a human heritage for oral and nonmaterial culture by UNESCO due to its symbolic and philosophical ties to the lives of Indonesians. However, the younger generation is gradually losing itslegacy because of technological and sociological changes that have influenced Indonesian batik. Consequently, batik knowledge is disappearing. A convolutional neural network and transfer learning techniques were utilized in deep learning to construct a model recognising batik motifs. The study utilized a dataset of

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Kambo, Edwin, Devotha Nyambo, Judith Leo, and Mussa Ally. "A Deep Learning Model for Classifying Black Sigatoka Disease in Banana Leaves Based on Infection Stages." Indian Journal Of Science And Technology 17, no. 37 (2024): 3889–97. http://dx.doi.org/10.17485/ijst/v17i37.2453.

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Objective: This research study aims to develop an efficient deep-learning model to detect and classify stages of Black Sigatoka disease in banana plants. Methods: In this study, deep learning techniques, specifically the basic Convolutional Neural Network (CNN) and VGG16 models, were used to address the challenge of identifying Black Sigatoka disease in banana leaves early on. The tests were conducted on a dataset containing labelled images of banana leaves, assessing their effectiveness based on criteria such as accuracy, precision, recall, and F1-score after adjusting hyperparameters for opt

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Aytekin, Alper, and Vasfiye Mençik. "Detection of Driver Dynamics with VGG16 Model." Applied Computer Systems 27, no. 1 (2022): 83–88. http://dx.doi.org/10.2478/acss-2022-0009.

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Abstract One of the most important factors triggering the occurrence of traffic accidents is that drivers continue to drive in a tired and drowsy state. It is a great opportunity to regularly control the dynamics of the driver with transfer learning methods while driving, and to warn the driver in case of possible drowsiness and to focus their attention in order to prevent traffic accidents due to drowsiness. A classification study was carried out with the aim of detecting the drowsiness of the driver by the position of the eyelids and the presence of yawning movement using the Convolutional N

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Nur Hidayat, Muhammad Ali. "Identifikasi varietas anggur menggunakan model CNN - VGG16." Journal Of Technology and Information System (J-TIS) 3, no. 1 (2024): 36–42. http://dx.doi.org/10.70129/jtis.v3i1.663.

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Vitis vinifera atau anggur adalah tanaman buah yang memiliki berbagai jenis varietas, berdasarkan data Vitis International Variety Catalogue (VIVC) terdapat 23.000 varietas anggur, secara fisik kita dapat mengenali varietas anggur dengan buah dan daun. Mengenali tanaman anggur menggunakan buah memerlukan waktu yang lama karena harus menunggu sampai anggur berbuah, sedangkan mengenali varietas anggur menggunakan daun membutuhkan waktu yang cepat tetapi tidak semua orang dapat mengenali varietas tanaman anggur, ciri daun anggur berbagai varietas mimiliki kemiripan sehingga sulit untuk diidentifi

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Asadoorian, Nazeh, Shokufeh Yaraghi, and Araeek Tahmasian. "Pre-trained quantum convolutional neural network for COVID-19 disease classification using computed tomography images." PeerJ Computer Science 10 (October 18, 2024): e2343. http://dx.doi.org/10.7717/peerj-cs.2343.

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Background The COVID-19 pandemic has had a significant influence on economies and healthcare systems around the globe. One of the most important strategies that has proven to be effective in limiting the disease and reducing its rapid spread is early detection and quick isolation of infections. Several diagnostic tools are currently being used for COVID-19 detection using computed tomography (CT) scan and chest X-ray (CXR) images. Methods In this study, a novel deep learning-based model is proposed for rapid detection of COVID-19 using CT-scan images. The model, called pre-trained quantum conv

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Singh, Dilbag, Yavuz Selim Taspinar, Ramazan Kursun, et al. "Classification and Analysis of Pistachio Species with Pre-Trained Deep Learning Models." Electronics 11, no. 7 (2022): 981. http://dx.doi.org/10.3390/electronics11070981.

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Pistachio is a shelled fruit from the anacardiaceae family. The homeland of pistachio is the Middle East. The Kirmizi pistachios and Siirt pistachios are the major types grown and exported in Turkey. Since the prices, tastes, and nutritional values of these types differs, the type of pistachio becomes important when it comes to trade. This study aims to identify these two types of pistachios, which are frequently grown in Turkey, by classifying them via convolutional neural networks. Within the scope of the study, images of Kirmizi and Siirt pistachio types were obtained through the computer v

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Hwang, Jung, Jae Seo, Jeong Kim, Suyoung Park, Young Kim, and Kwang Kim. "Comparison between Deep Learning and Conventional Machine Learning in Classifying Iliofemoral Deep Venous Thrombosis upon CT Venography." Diagnostics 12, no. 2 (2022): 274. http://dx.doi.org/10.3390/diagnostics12020274.

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In this study, we aimed to investigate quantitative differences in performance in terms of comparing the automated classification of deep vein thrombosis (DVT) using two categories of artificial intelligence algorithms: deep learning based on convolutional neural networks (CNNs) and conventional machine learning. We retrospectively enrolled 659 participants (DVT patients, 282; normal controls, 377) who were evaluated using contrast-enhanced lower extremity computed tomography (CT) venography. Conventional machine learning consists of logistic regression (LR), support vector machines (SVM), ran

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Noprisson, Handrie. "Fine-Tuning Model Transfer Learning VGG16 Untuk Klasifikasi Citra Penyakit Tanaman Padi." JSAI (Journal Scientific and Applied Informatics) 5, no. 3 (2022): 244–49. http://dx.doi.org/10.36085/jsai.v5i3.3609.

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Besarnya tingkat permintaan atas komoditas ini mendorong adanya perbaikan hasil pertanian dengan cara mengatasi penyakit pada tanaman padi. Deteksi penyakit pada tanaman padi sejak awal penanaman akan mengurangi dampak pertumbuhan tanaman cukup signifikan. Dengan adanya perawatan yang tepat dari hasil identifikasi kasus penyakit sejak ini akan menambah produktivitas hasil pertanian. Penelitian ini bertujuan membuat analisis kinerja klasifikasi penyakit tanaman padi convolution neural net (CNN) dengan arsitektur VGG16 menggunakan fine-tuning. Untuk meproses dataset dan mengelompokkan data menja

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Fatima, Tanjim, and Hamdy Soliman. "Application of VGG16 Transfer Learning for Breast Cancer Detection." Information 16, no. 3 (2025): 227. https://doi.org/10.3390/info16030227.

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Breast cancer is among the primary causes of cancer-related deaths globally, highlighting the critical need for effective and early diagnostic methods. Traditional diagnostic approaches, while valuable, often face limitations in accuracy and accessibility. Recent advancements in deep learning, particularly transfer learning, provide promising solutions for enhancing diagnostic precision in breast cancer detection. Due to the limited capability of the BreakHis dataset, transfer learning was utilized to advance the training of our new model with the VGG16 neural network model, well trained on th

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Arya Kala. "Optimization of Convolutional Neural Networks for Detecting Oral Cancer-Induced Bone Invasion: A Genetic Algorithm Approach." Advances in Nonlinear Variational Inequalities 28, no. 3s (2024): 01–11. https://doi.org/10.52783/anvi.v28.2843.

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Introduction: For patients diagnosed with oral squamous cell carcinoma (OSCC), determining whether the tumor has penetrated the bone is crucial for therapy planning and surgical procedures. Computed Tomography (CT) imaging is preferred by radiologists due to its high sensitivity and specificity in detecting bone invasion. Objectives In this study, we present a deep learning-based Convolutional Neural Network (CNN) model, optimized using a genetic algorithm, to automatically detect bone invasion in OSCC cases. Methods: First, CT-scan images were collected fro S. M.S. hospital, Jaipur and annota

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Arya Kala. "Optimization of Convolutional Neural Networks for Detecting Oral Cancer-Induced Bone Invasion: A Genetic Algorithm Approach." Advances in Nonlinear Variational Inequalities 28, no. 2s (2024): 12–21. https://doi.org/10.52783/anvi.v28.2503.

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Introduction: For patients diagnosed with oral squamous cell carcinoma (OSCC), determining whether the tumor has penetrated the bone is crucial for therapy planning and surgical procedures. Computed Tomography (CT) imaging is preferred by radiologists due to its high sensitivity and specificity in detecting bone invasion. Objectives In this study, we present a deep learning-based Convolutional Neural Network (CNN) model, optimized using a genetic algorithm, to automatically detect bone invasion in OSCC cases. Methods: First, CT-scan images were collected fro S. M.S. hospital, Jaipur and annota

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Minh Ly Duc and Dong Phan Tan. "Face Gender Classification by Using Improve Binary Particle Swarm Optimization and K-Nearest Neighbors." international journal of engineering technology and management sciences 7, no. 5 (2023): 269–77. http://dx.doi.org/10.46647/ijetms.2023.v07i05.031.

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This paper studies the application of the Binary particle swarm optimization (BPSO) algorithm to the optimal search for facial features and gender classification by K-Nearest Neighbors (K-NN) model. The results show that the accuracy and processing time of the model is much better than that of VGG16, VGG19, Resnet50, Senet50, Face Net, Open Face and FbDeep Face models. a large-scale GenderFace80K dataset with 80,000 facial images with gender annotation used in the research model.

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Khan, Inam Ullah, Mohaimenul Azam Khan Raiaan, Kaniz Fatema, et al. "A Computer-Aided Diagnostic System to Identify Diabetic Retinopathy, Utilizing a Modified Compact Convolutional Transformer and Low-Resolution Images to Reduce Computation Time." Biomedicines 11, no. 6 (2023): 1566. http://dx.doi.org/10.3390/biomedicines11061566.

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Diabetic retinopathy (DR) is the foremost cause of blindness in people with diabetes worldwide, and early diagnosis is essential for effective treatment. Unfortunately, the present DR screening method requires the skill of ophthalmologists and is time-consuming. In this study, we present an automated system for DR severity classification employing the fine-tuned Compact Convolutional Transformer (CCT) model to overcome these issues. We assembled five datasets to generate a more extensive dataset containing 53,185 raw images. Various image pre-processing techniques and 12 types of augmentation

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Wu, Shengbin. "Expression Recognition Method Using Improved VGG16 Network Model in Robot Interaction." Journal of Robotics 2021 (December 20, 2021): 1–9. http://dx.doi.org/10.1155/2021/9326695.

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Aiming at the problems of poor representation ability and less feature data when traditional expression recognition methods are applied to intelligent applications, an expression recognition method based on improved VGG16 network is proposed. Firstly, the VGG16 network is improved by using large convolution kernel instead of small convolution kernel and reducing some fully connected layers to reduce the complexity and parameters of the model. Then, the high-dimensional abstract feature data output by the improved VGG16 is input into the convolution neural network (CNN) for training, so as to o

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Choi, Wansuk, and Seoyoon Heo. "Deep Learning Approaches to Automated Video Classification of Upper Limb Tension Test." Healthcare 9, no. 11 (2021): 1579. http://dx.doi.org/10.3390/healthcare9111579.

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The purpose of this study was to classify ULTT videos through transfer learning with pre-trained deep learning models and compare the performance of the models. We conducted transfer learning by combining a pre-trained convolution neural network (CNN) model into a Python-produced deep learning process. Videos were processed on YouTube and 103,116 frames converted from video clips were analyzed. In the modeling implementation, the process of importing the required modules, performing the necessary data preprocessing for training, defining the model, compiling, model creation, and model fit were

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Goyal, Aviral, Hrishikesh Magadum, Sanskriti Srivastava, Harpal Patel, and Dr Jaisankar N. "COVID-19 SEVERITY PREDICTION USING DEEP LEARNING MODELS – COMPARATIVE STUDY." International Journal of Engineering Applied Sciences and Technology 8, no. 2 (2023): 101–6. http://dx.doi.org/10.33564/ijeast.2023.v08i02.014.

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In response to the exponential rise in COVID19 instances, healthcare professionals have been seeking for strict automated detection measures to stop COVID from spreading while simultaneously attempting to restrict device processing needs. Additionally, we must comprehend the severity of the COVID infection based on the harm the infection has done to the lungs to properly treat impacted individuals. We choose a suitable CNN model as a result after performing an initial comparison analysis of many well-known CNN models. Using a dataset generated from sources like prior papers and other internet

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Jianqing Ye. "Prediction Model of Ancient Village Pottery Building Microspace Design Style by Integrating Machine Learning." Journal of Electrical Systems 20, no. 6s (2024): 425–36. http://dx.doi.org/10.52783/jes.2667.

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Microspace design styles on pottery fragments offer a captivating window into the artistic heritage and cultural practices of past societies. These intricate details and recurring patterns hold valuable clues about rituals, beliefs, and artistic expressions. However, existing approaches struggle to capture the relationships between design elements within a single piece. This limitation hinders the ability to capture the holistic meaning conveyed by the microspace design style. To overcome this limitation, this research proposes a novel approach called MoANN-DSOA for predicting microspace desig

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Karac, Abdulkadir. "Predicting COVID-19 Cases on a Large Chest X-Ray Dataset Using Modified Pre-trained CNN Architectures." Applied Computer Systems 28, no. 1 (2023): 44–57. http://dx.doi.org/10.2478/acss-2023-0005.

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Abstract The Coronavirus is a virus that spreads very quickly. Therefore, it has had very destructive effects in many areas worldwide. Because X-ray images are an easily accessible, fast, and inexpensive method, they are widely used worldwide to diagnose COVID-19. This study tried detecting COVID-19 from X-ray images using pre-trained VGG16, VGG19, InceptionV3, and Resnet50 CNN architectures and modified versions of these architectures. The fully connected layers of the pre-trained architectures have been reorganized in the modified CNN architectures. These architectures were trained on binary

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