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Journal articles on the topic 'VGG19 Algorithm'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Zhang, Xinle, Jian Cui, Huanjun Liu, et al. "Weed Identification in Soybean Seedling Stage Based on Optimized Faster R-CNN Algorithm." Agriculture 13, no. 1 (2023): 175. http://dx.doi.org/10.3390/agriculture13010175.

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Soybean in the field has a wide range of intermixed weed species and a complex distribution status, and the weed identification rate of traditional methods is low. Therefore, a weed identification method is proposed based on the optimized Faster R-CNN algorithm for the soybean seedling. Three types of weed datasets, including soybean, with a total of 9816 photos were constructed, and cell phone photo data were used for training and recognition. Firstly, by comparing the classification effects of ResNet50, VGG16, and VGG19, VGG19 was identified as the best backbone feature extraction network fo

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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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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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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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Wan, Xiang, Xiangyu Zhang, and Lilan Liu. "An Improved VGG19 Transfer Learning Strip Steel Surface Defect Recognition Deep Neural Network Based on Few Samples and Imbalanced Datasets." Applied Sciences 11, no. 6 (2021): 2606. http://dx.doi.org/10.3390/app11062606.

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The surface defects’ region of strip steel is small, and has various defect types and, complex gray structures. There tend to be a large number of false defects and edge light interference, which lead traditional machine vision algorithms to be unable to detect defects for various types of strip steel. Image detection techniques based on deep learning require a large number of images to train a network. However, for a dataset with few samples with category imbalanced defects, common deep learning neural network training tasks cannot be carried out. Based on rapid image preprocessing algorithms

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Prasetyo, Simeon Yuda, Ghinaa Zain Nabiilah, Zahra Nabila Izdihar, and Sani Muhamad Isa. "Pneumonia Detection on X-Ray Imaging using Softmax Output in Multilevel Meta Ensemble Algorithm of Deep Convolutional Neural Network Transfer Learning Models." International Journal of Advances in Intelligent Informatics 9, no. 2 (2023): 319. http://dx.doi.org/10.26555/ijain.v9i2.884.

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Pneumonia is the leading cause of death from a single infection worldwide in children. A proven clinical method for diagnosing pneumonia is through a chest X-ray. However, the resulting X-ray images often need clarification, resulting in subjective judgments. In addition, the process of diagnosis requires a longer time. One technique can be applied by applying advanced deep learning, namely, Transfer Learning with Deep Convolutional Neural Network (Deep CNN) and modified Multilevel Meta Ensemble Learning using Softmax. The purpose of this research was to improve the accuracy of the pneumonia c

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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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Xu, Zhijing, Yuhao Huo, Kun Liu, and Sidong Liu. "Detection of ship targets in photoelectric images based on an improved recurrent attention convolutional neural network." International Journal of Distributed Sensor Networks 16, no. 3 (2020): 155014772091295. http://dx.doi.org/10.1177/1550147720912959.

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Deep learning algorithms have been increasingly used in ship image detection and classification. To improve the ship detection and classification in photoelectric images, an improved recurrent attention convolutional neural network is proposed. The proposed network has a multi-scale architecture and consists of three cascading sub-networks, each with a VGG19 network for image feature extraction and an attention proposal network for locating feature area. A scale-dependent pooling algorithm is designed to select an appropriate convolution in the VGG19 network for classification, and a multi-fea

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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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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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Hamdi, Mohammed, Ebrahim Mohammed Senan, Bakri Awaji, Fekry Olayah, Mukti E. Jadhav, and Khaled M. Alalayah. "Analysis of WSI Images by Hybrid Systems with Fusion Features for Early Diagnosis of Cervical Cancer." Diagnostics 13, no. 15 (2023): 2538. http://dx.doi.org/10.3390/diagnostics13152538.

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Cervical cancer is one of the most common types of malignant tumors in women. In addition, it causes death in the latter stages. Squamous cell carcinoma is the most common and aggressive form of cervical cancer and must be diagnosed early before it progresses to a dangerous stage. Liquid-based cytology (LBC) swabs are best and most commonly used for cervical cancer screening and are converted from glass slides to whole-slide images (WSIs) for computer-assisted analysis. Manual diagnosis by microscopes is limited and prone to manual errors, and tracking all cells is difficult. Therefore, the de

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Mohan, Ramya, Seifedine Kadry, Venkatesan Rajinikanth, Arnab Majumdar, and Orawit Thinnukool. "Automatic Detection of Tuberculosis Using VGG19 with Seagull-Algorithm." Life 12, no. 11 (2022): 1848. http://dx.doi.org/10.3390/life12111848.

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Due to various reasons, the incidence rate of communicable diseases in humans is steadily rising, and timely detection and handling will reduce the disease distribution speed. Tuberculosis (TB) is a severe communicable illness caused by the bacterium Mycobacterium-Tuberculosis (M. tuberculosis), which predominantly affects the lungs and causes severe respiratory problems. Due to its significance, several clinical level detections of TB are suggested, including lung diagnosis with chest X-ray images. The proposed work aims to develop an automatic TB detection system to assist the pulmonologist

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Elnakib, Ahmed, Hanan M. Amer, and Fatma E.Z. Abou-Chadi. "Early Lung Cancer Detection using Deep Learning Optimization." International Journal of Online and Biomedical Engineering (iJOE) 16, no. 06 (2020): 82. http://dx.doi.org/10.3991/ijoe.v16i06.13657.

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This paper proposes a Computer Aided Detection (CADe) system for early detection of lung nodules from low dose computed tomography (LDCT) images. The proposed system initially pre-process the raw data to improve the contrast of the low dose images. Compact deep learning features are then extracted by investigating different deep learning architectures, including Alex, VGG16, and VGG19 networks. To optimize the extracted set of features, a genetic algorithm (GA) is trained to select the most relevant features for early detection. Finally, different types of classifiers are tested in order to ac

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Dudekula, Usen, and Purnachand N. "Linear fusion approach to convolutional neural networks for facial emotion recognition." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 3 (2022): 1489. http://dx.doi.org/10.11591/ijeecs.v25.i3.pp1489-1500.

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Facial expression recognition is a challenging problem in the scientific field of computer vision. Several face expression recognition (FER) algorithms are proposed in the field of machine learning, and deep learning to extract expression knowledge from facial representations. Even though numerous algorithms have been examined, several issues like lighting changes, rotations and occlusions. We present an efficient approach to enhance recognition accuracy in this study, advocates transfer learning to fine-tune the parameters of the pre-trained model (VGG19 model ) and non-pre-trained model conv

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Jain1, Saloni, Sunita GP2, and Sampath Kumar S3. "Tire Texture Monitoring (VGG 19 VS Efficient Net b7)." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 07 (2024): 1–12. http://dx.doi.org/10.55041/ijsrem36751.

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Tires are crucial components of vehicles, continuously in contact with the road. Monitoring tire conditions is vital for safety and performance, as degradation in tire treads and sidewalls can affect traction, fuel efficiency, longevity, and road noise. This research leverages both VGG19 and Efficient Net B7 algorithms to enhance tire image rendering, addressing limitations of traditional techniques. Using a binary classification algorithm, we classify tire images as healthy or cracked. By fine-tuning VGG19 and EfficientNet B7 on a specialized tire dataset, we achieve high-quality, photorealis

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Saputra, Rizal Amegia, Toto Haryanto, and Sri Wasyianti. "ABILITY CONVOLUTIONAL FEATURE EXTRACTION FOR CHILI LEAF DISEASE USING SUPPORT VECTOR MACHINE CLASSIFICATION." Jurnal Pilar Nusa Mandiri 20, no. 1 (2024): 25–32. http://dx.doi.org/10.33480/pilar.v20i1.4961.

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Chili plants are among the most commonly used food ingredients in various dishes in Indonesia. Leaves on chili plants are often affected by disease; if the disease is not treated immediately, it can damage the plant and cause crop failure. Early detection of chili plant diseases is important to reduce the risk of crop failure. The development of technology and the application of machine-learning algorithms can automatically monitor chili plants using a computer system. Using this algorithm, the system analyzes and identifies diseases that a camera can observe and record. In this study, the pro

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Chung, Kyung Min, Hyunjae Yu, Jong-Ho Kim, et al. "Deep Learning-Based Knee MRI Classification for Common Peroneal Nerve Palsy with Foot Drop." Biomedicines 11, no. 12 (2023): 3171. http://dx.doi.org/10.3390/biomedicines11123171.

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Foot drop can have a variety of causes, including the common peroneal nerve (CPN) injuries, and is often difficult to diagnose. We aimed to develop a deep learning-based algorithm that can classify foot drop with CPN injury in patients with knee MRI axial images only. In this retrospective study, we included 945 MR image data from foot drop patients confirmed with CPN injury in electrophysiologic tests (n = 42), and 1341 MR image data with non-traumatic knee pain (n = 107). Data were split into training, validation, and test datasets using a 8:1:1 ratio. We used a convolution neural network-ba

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Dudekula, Usen, and Purnachand N. "Linear fusion approach to convolutional neural networks for facial emotion recognition." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 3 (2022): 1489–500. https://doi.org/10.11591/ijeecs.v25.i3.pp1489-1500.

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Facial expression recognition is a challenging problem in the scientific field of computer vision. Several face expression recognition (FER) algorithms are proposed in the field of machine learning, and deep learning to extract expression knowledge from facial representations. Even though numerous algorithms have been examined, several issues like lighting changes, rotations and occlusions. We present an efficient approach to enhance recognition accuracy in this study, advocates transfer learning to fine-tune the parameters of the pre-trained model (VGG19 model) and non-pre-trained model convo

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Aakash, S. Amutha, D. Nandhini, and Ansh. "Classification and Validation of Tomato Leaf Disease Using Deep Learning Techniques." International Journal on Engineering Artificial Intelligence Management, Decision Support, and Policies 1, no. 1 (2024): 41–60. https://doi.org/10.63503/j.ijaimd.2024.9.

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Tomatoes are regarded as fruits since they fit the botanical definition of a fruit because they are the fleshy parts of a plant that enclose its seeds. There are approximately 10 different kinds of diseases for a tomato plant, which is huge in number and can create huge losses for the farmers. This paper focuses on the classification of tomato plant leaf diseases using Convolution Neural Net-work (CNN) a deep learning technique that is especially employed for image recognition and pixel data processing activities. CNN has been used to identify whether the given photo of the plant is of a healt

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Habie, Khairul Fathan, Murinto Murinto, Sunardi Sunardi, and Arfiani Nur Khusna. "Effect of Learning Rate on VGG19 Model Architecture for Human Skin Disease Classification." Decode: Jurnal Pendidikan Teknologi Informasi 4, no. 3 (2024): 1071–81. https://doi.org/10.51454/decode.v4i3.576.

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The skin is the largest external organ that serves to protect human internal organs and is very sensitive to various diseases, so early detection is very important to reduce the risk and increase the chance of recovery. This study aims to classify skin disease types using CNN algorithm with VGG19 architecture and learning rate adjustment to get a more optimal model, using a dataset from Kaggle consisting of 3,295 images with six classes, including several types of skin diseases and one healthy skin class. The preprocessing process includes dividing the data into training and testing sets, resi

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A.M. Prasanna Kumar, Vijaya S.M, and Bharathi G. "Hybrid Reverse Propagation ANN Adaptive Algorithm Based Deep Learning Image Processing for Pneumonia Detection." ACS Journal for Science and Engineering 2, no. 2 (2022): 36–48. http://dx.doi.org/10.34293/acsjse.v2i2.37.

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Pneumonia is a syndrome that is caused by bacterial lung disease. This disease is diagnosed using a chest X-ray. Early diagnosis is important for successful treatment. This disease can be diagnosed using X-rays. Sometimes it can be confused with another bacterial disease due to an unclear chest X-ray. Consequently, we need a computer-aided diagnostic system to guide doctors. In this, amalgam backhaul algorithms are introduced to achieve multilayer network erudition. System noise investigation is done using artificial neural network (ANN). The vgg19 convolution neural network model was used to

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Based, Md Abdul, Elias Ur ,. Rahman, and Mohammad Shorif Uddin. "Development of an Electronic Voting System using Blockchain Technology and Deep Hybrid Learning." WSEAS TRANSACTIONS ON COMPUTERS 23 (August 14, 2024): 194–203. http://dx.doi.org/10.37394/23205.2024.23.18.

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Democratic people cannot function properly in today's sophisticated societies (where voting is a prominent issue) without electronic voting technologies. This study explores the use of hybrid learning algorithms for biometric authentication of voters, and blockchain technology for secure electronic voting. The thorough analysis includes a collection of more than 50,000 fingerprint samples using custom Convolutional Neural Network (CNN), VGG16, VGG19, Xception, and Inception. The algorithms are evaluated using F1-score, recall, accuracy, and precision. By combining Random Forest with a speciall

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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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Muliawan, Nicholas Hans, Edbert Valencio Angky, and Simeon Yuda Prasetyo. "Age estimation through facial images using Deep CNN Pretrained Model and Particle Swarm Optimization." E3S Web of Conferences 426 (2023): 01041. http://dx.doi.org/10.1051/e3sconf/202342601041.

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There has been a lot of recent study on age estimates utilizing different optimization techniques, architecture models, and diverse strategies with some variations. However, accuracy improvement in age estimation studies remains a challenge due to the inability of traditional approaches to effectively capture complex facial features and variations. Therefore, this study investigates the usage of Particle Swarm Optimization in Deep CNN models to improve accuracy. The focus of the study is on exploring different feature extractors for the age estimation task, utilizing pre-trained CNN models suc

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Han, Baoru, Jinglong Du, Yuanyuan Jia, and Huazheng Zhu. "Zero-Watermarking Algorithm for Medical Image Based on VGG19 Deep Convolution Neural Network." Journal of Healthcare Engineering 2021 (July 1, 2021): 1–12. http://dx.doi.org/10.1155/2021/5551520.

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Aiming at the security issues in the storage and transmission of medical images in the medical information system, combined with the special requirements of medical images for the protection of lesion areas, this paper proposes a robust zero-watermarking algorithm for medical images’ security based on VGG19. First, the pretrained VGG19 is used to extract deep feature maps of medical images, which are fused into the feature image. Second, the feature image is transformed by Fourier transform, and low-frequency coefficients of the Fourier transform are selected to construct the feature matrix of

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Pan, Shen, and Zhanyuan Chang. "WD-1D-VGG19-FEA: An Efficient Wood Defect Elastic Modulus Predictive Model." Sensors 24, no. 17 (2024): 5572. http://dx.doi.org/10.3390/s24175572.

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As a mature non-destructive testing technology, near-infrared (NIR) spectroscopy can effectively identify and distinguish the structural characteristics of wood. The Wood Defect One-Dimensional Visual Geometry Group 19-Finite Element Analysis (WD-1D-VGG19-FEA) algorithm is used in this study. 1D-VGG19 classifies the near-infrared spectroscopy data to determine the knot area, fiber deviation area, transition area, and net wood area of the solid wood board surface and generates a two-dimensional image of the board surface through inversion. Then, the nonlinear three-dimensional model of wood wit

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Gururaj, Vaishnavi, Shriya Varada Ramesh, Sanjana Satheesh, Ashwini Kodipalli, and Kusuma Thimmaraju. "Analysis of deep learning frameworks for object detection in motion." International Journal of Knowledge-based and Intelligent Engineering Systems 26, no. 1 (2022): 7–16. http://dx.doi.org/10.3233/kes-220002.

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Object detection and recognition is a computer vision technology and is considered as one of the challenging tasks in the field of computer vision. Many approaches for detection have been proposed in the past. AIM: This paper is mainly aiming to discuss the existing detection and classification techniques of Deep Convolutional Neural Networks (CNN) with an importance placed on highlighting the training and accuracy of the different CNN models. METHODS: In the proposed work, Faster RCNN, YOLO and SSD are used to detect helmets. OUTCOME: The survey says MobileNets has higher accuracy when compar

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Rasheed, Zahid, Yong-Kui Ma, Inam Ullah, et al. "Automated Classification of Brain Tumors from Magnetic Resonance Imaging Using Deep Learning." Brain Sciences 13, no. 4 (2023): 602. http://dx.doi.org/10.3390/brainsci13040602.

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Brain tumor classification is crucial for medical evaluation in computer-assisted diagnostics (CAD). However, manual diagnosis of brain tumors from magnetic resonance imaging (MRI) can be time-consuming and complex, leading to inaccurate detection and classification. This is mainly because brain tumor identification is a complex procedure that relies on different modules. The advancements in Deep Learning (DL) have assisted in the automated process of medical images and diagnostics for various medical conditions, which benefits the health sector. Convolutional Neural Network (CNN) is one of th

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Abraheem, Salih, Ziyodulla Yusupov, Javad Rahebi, and Raheleh Ghadami. "Advanced Solar Panel Fault Detection Using VGG19 and Jellyfish Optimization." Processes 13, no. 7 (2025): 2021. https://doi.org/10.3390/pr13072021.

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Solar energy has become a vital renewable energy source (RES), and photovoltaic (PV) systems play a key role in its utilization. However, the performance of these systems can be compromised by faulty panels. This paper proposes an innovative framework that combines the deep neural network VGG19 with the Jellyfish Optimization Search Algorithm (JFOSA) for efficient fault detection using aerial images. VGG19 excels in automatic feature extraction, while JFOSA optimizes feature selection and significantly improves classification performance. The new framework achieves impressive results, includin

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Maheswari, T. N. Uma, Ankita Bohra, and S. Sharuniveda. "Pioneering a New Frontier: Artificial Intelligence (AI)-Driven Lip Print Pattern Analysis—A Systematic Review." Journal of Indian Academy of Oral Medicine and Radiology 36, no. 3 (2024): 206–12. https://doi.org/10.4103/jiaomr.jiaomr_132_24.

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Abstract Background: Artificial intelligence (AI) technology is a trailblazer in obtaining critical information in oral observational studies involving forensic medicine. It holds immense potential in forensic odontology. Machine learning’s involvement in our daily lives has doubled. The constructive upgraded mechanism of machine learning involved in exploring lip print patterns is a crucial perspective for future diagnostic studies. Objectives: To illuminate the significance and application of AI technology in forensic odontology, specifically lip print analysis. The cardinal objective is to

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Azhar, Yufis, Fauzan Adrivano Setiono, and Didih Rizki Chandranegara. "Comparison of Transfer Learning Models in Classification Dental and Tongue Disease Images." Journal of Electronics, Electromedical Engineering, and Medical Informatics 7, no. 1 (2024): 117–29. https://doi.org/10.35882/jeeemi.v7i1.487.

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According to the Global Burden of Disease Study, dental caries is the most prevalent oral health ailment, affecting around 3.5 billion individuals globally. According to the Ministry of Health of the Republic of Indonesia, 93% of children in the country suffer from oral health issues, making poor oral health a serious public health concern. The tongue and teeth in the mouth are particularly vulnerable to a wide range of illnesses, and the condition of the mouth is a key sign of the health of the body as a whole. The CNN algorithm has been utilized in numerous studies to classify disorders of t

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Shukhaev, S. V., E. A. Mordovtseva, E. A. Pustozerov, and S. S. Kudlakhmedov. "Application of convolutional neural networks to define Fuchs endothelial dystrophy." Fyodorov journal of ophthalmic surgery, no. 1S (February 17, 2023): 70–76. http://dx.doi.org/10.25276/0235-4160-2022-4s-70-76.

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Purpose. To evaluate the application of convolutional neural networks for the automatic detection of Fuchs' dystrophy. Material and methods. The study included 700 biomicroscopic images of the corneal endothelium (Tomey EM-3000) randomly selected from the database of the Saint-Petersburg brunch of the S. Fyodorov Eye Microsurgery Federal State Institution. At the first stage, the images were divided into 2 groups. The first group included images with the presence of Fuchs' dystrophy, the second – another pathology or a healthy cornea. The corneal endothelial cell density images were divided in

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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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Han, Xianjing, and Guoxin Liang. "Echocardiographic Features of Patients with Coronary Heart Disease and Angina Pectoris under Deep Learning Algorithms." Scientific Programming 2021 (November 13, 2021): 1–8. http://dx.doi.org/10.1155/2021/8336959.

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Based on the VGG19-fully convolutional network (FCN) (VGG19-FCN) and U-Net model in the deep learning algorithms, the left ventricle in the ultrasonic cardiogram was segmented automatically. In addition, this study evaluated the value of ultrasonic cardiogram features after segmentation by the optimized algorithm in diagnosing patients with coronary heart disease (CHD) and angina pectorisody; patients with arrhythmia; and pa. In this study, 30 patients with confirmed CHD and 30 normal people without CHD from the same hospital in a certain area were selected as the research objects. Firstly, th

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Cagadas, Dominic Olango, Dwi Sudarno Putra, Kristine Mae Paboreal Dunque, and Meri Azmi. "Classifying four maturity categories of coffee cherry using CNN-VGG19." Teknomekanik 7, no. 2 (2024): 176–84. https://doi.org/10.24036/teknomekanik.v7i2.31072.

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The local coffee farmers employ manual inspection to identify the maturity of coffee cherries that are inefficient in labor and time. Thus, the objective of this study is to develop a CNN-VGG19 algorithm model that can accurately detect the maturity image of coffee cherry samples, and classify them into: unripe, semi-ripe, ripe, and overripe categories. The proposed solution will provide local coffee farmers with an automated and more accurate classification of the quality of coffee cherries. The visual geometry group-19 was employed to increase the object recognition model performance of the

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Xu, Yihao. "CNN-based image style transformation--Using VGG19." Applied and Computational Engineering 39, no. 1 (2024): 130–36. http://dx.doi.org/10.54254/2755-2721/39/20230589.

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eural Style Transfer is a widely used approach in the field of computer vision, which aims to generate visual effects by integrating the information contained in one image into another. In this paper, this work presents an implementation of neural style transfer using TensorFlow and the VGG19 model. The proposed method involves loading and preprocessing the content and style images, extracting features from both images using the VGG19 model, and computing Gram matrices to capture the style information. A StyleContentModel class is introduced to encapsulate the style and content extraction proc

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Jollyta, Deny, Prihandoko Prihandoko, Johan Johan, William Ramdhan, and Erick Santoso. "Transfer Learning Model Evaluation on CNN Algorithm: Indonesian Sign Language System (SIBI)." Journal of Applied Business and Technology 6, no. 2 (2025): 83–92. https://doi.org/10.35145/jabt.v6i2.213.

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In Indonesia as much as elsewhere, the deaf can communicate using sign language. The Indonesian Sign Language System (SIBI) is one of the sign language systems used in Indonesia. A model produced by the Convolutional Neural Network (CNN) method can be used in computer science for the recognition of sign language. By using the Transfer Learning paradigm, CNN's performance may be enhanced. However, not many researches have been conducted to assess the effectiveness of transfer learning on sign language models, particularly those that use the TensorFlow library. In fact, the evaluation results ca

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Zhao, Dechun, Zixin Luo, Mingcai Yao, Li Wei, Lu Qin, and Ziqiong Wang. "State identification of Parkinson’s disease based on transfer learning." Technology and Health Care 32, no. 6 (2024): 4097–107. http://dx.doi.org/10.3233/thc-231929.

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BACKGROUND: The local field potential (LFP) signals are a vital signal for studying the mechanisms of deep brain stimulation (DBS) and constructing adaptive DBS containing information related to the motor symptoms of Parkinson’s disease (PD). OBJECTIVE: A Parkinson’s disease state identification algorithm based on the feature extraction strategy of transfer learning was proposed. METHODS: The algorithm uses continuous wavelet transform (CWT) to convert one-dimensional LFP signals into two-dimensional gray-scalogram images and color images respectively, and designs a Bayesian optimized random f

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Mohammad, Farah, and Saad Al Ahmadi. "Alzheimer’s Disease Prediction Using Deep Feature Extraction and Optimization." Mathematics 11, no. 17 (2023): 3712. http://dx.doi.org/10.3390/math11173712.

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Alzheimer’s disease (AD) is a prevalent neurodegenerative disorder that affects a substantial proportion of the population. The accurate and timely prediction of AD carries considerable importance in enhancing the diagnostic process and improved treatment. This study provides a thorough examination of AD prediction using the VGG19 deep learning model. The primary objective of this study is to investigate the effectiveness of feature fusion and optimization techniques in enhancing the accuracy of classification. The generation of a comprehensive feature map is achieved through the fusion of fea

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Ikawati, Vidya, Indrasary Yoeni, and Ary Setijadi Prihatmanto. "Prediction of Covid-19 Disease Using X-Ray Images with Deep Learning Algorithm." Journal of Applied Science and Advanced Engineering 1, no. 1 (2023): 28–34. http://dx.doi.org/10.59097/jasae.v1i1.11.

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The capacity of Indonesian medical personnel, especially pulmonary and radiology specialists, is still far from the proportionate ratio of Indonesia's population. This limitation is one of Indonesia's main issues in realizing adequate health services for lung sufferers. Furthermore, the diagnosis process is one of the keys to obtaining appropriate and fast treatment procedures for sufferers. This paper will review the research conducted by the PPTIK ITB team in developing a tool for diagnosing lung disease with the help of Deep Learning. In this study, deep learning models play a role in class

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Deeba, Kannan, Sattianadan Dasarathan, Srinivasa Rao Kandula, et al. "Early fire detection technique for human being using deep learning algorithm." Indonesian Journal of Electrical Engineering and Computer Science 31, no. 3 (2023): 1648. http://dx.doi.org/10.11591/ijeecs.v31.i3.pp1648-1655.

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Fire and smoke detection in today’s world is a must, especially in clustered areas where a quick response can prevent significant damages and save lives. Early detection plays a significant role in preventing the fire from spreading by alerting the emergency response personnel. It may not be possible to install traditional fire and smoke detectors everywhere. As a result, incorporating fire and smoke detection into existing closed circuit television (CCTV) systems in various places can provide a warning to the appropriate authorities, allowing for quick action to prevent the fire from spreadin

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Kannan, Deeba, Dasarathan Sattianadan, Rao Kandula Srinivasa, et al. "Early fire detection technique for human being using deep learning algorithm." Early fire detection technique for human being using deep learning algorithm 31, no. 3 (2023): 1648–55. https://doi.org/10.11591/ijeecs.v31.i3.pp1648-1655.

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Fire and smoke detection in today’s world is a must, especially in clustered areas where a quick response can prevent significant damages and save lives. Early detection plays a significant role in preventing the fire from spreading by alerting the emergency response personnel. It may not be possible to install traditional fire and smoke detectors everywhere. As a result, incorporating fire and smoke detection into existing closed circuit television (CCTV) systems in various places can provide a warning to the appropriate authorities, allowing for quick action to prevent the fire from sp

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Bani-Hani, Raed M., Ahmed S. Shatnawi, and Lana Al-Yahya. "Vulnerability Detection and Classification of Ethereum Smart Contracts Using Deep Learning." Future Internet 16, no. 9 (2024): 321. http://dx.doi.org/10.3390/fi16090321.

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Smart contracts are programs that reside and execute on a blockchain, like any transaction. They are automatically executed when preprogrammed terms and conditions are met. Although the smart contract (SC) must be presented in the blockchain for the integrity of data and transactions stored within it, it is highly exposed to several vulnerabilities attackers exploit to access the data. In this paper, classification and detection of vulnerabilities targeting smart contracts are performed using deep learning algorithms over two datasets containing 12,253 smart contracts. These contracts are conv

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Akter, Shamima, F. M. Javed Mehedi Shamrat, Sovon Chakraborty, Asif Karim, and Sami Azam. "COVID-19 Detection Using Deep Learning Algorithm on Chest X-ray Images." Biology 10, no. 11 (2021): 1174. http://dx.doi.org/10.3390/biology10111174.

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COVID-19, regarded as the deadliest virus of the 21st century, has claimed the lives of millions of people around the globe in less than two years. Since the virus initially affects the lungs of patients, X-ray imaging of the chest is helpful for effective diagnosis. Any method for automatic, reliable, and accurate screening of COVID-19 infection would be beneficial for rapid detection and reducing medical or healthcare professional exposure to the virus. In the past, Convolutional Neural Networks (CNNs) proved to be quite successful in the classification of medical images. In this study, an a

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Adnan, Muhammad, Muhammad Sardaraz, Muhammad Tahir, Muhammad Najam Dar, Mona Alduailij, and Mai Alduailij. "A Robust Framework for Real-Time Iris Landmarks Detection Using Deep Learning." Applied Sciences 12, no. 11 (2022): 5700. http://dx.doi.org/10.3390/app12115700.

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Iris detection and tracking plays a vital role in human–computer interaction and has become an emerging field for researchers in the last two decades. Typical applications such as virtual reality, augmented reality, gaze detection for customer behavior, controlling computers, and handheld embedded devices need accurate and precise detection of iris landmarks. A significant improvement has been made so far in iris detection and tracking. However, iris landmarks detection in real-time with high accuracy is still a challenge and a computationally expensive task. This is also accompanied with the

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Bandan, Sheikh Sadi, MD Samiul Islam Sabbir, Md Sharuf Hossain, and Khadiza Tul Kobra. "Leukemia Detection Revolution: AI and Machine Learning Enhance Image-Based Diagnosis." International Journal of Research and Innovation in Applied Science IX, no. VIII (2024): 632–41. http://dx.doi.org/10.51584/ijrias.2024.908057.

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Currently, several diseases have become epidemic in Bangladesh, one of them is leukemia. This disease usually affects people of any age. It is usually found in blood cells, blood plasma, bone marrow. If this disease has been going on for a long time and without specific treatment, it often does not turn into cancer. It is called leukemia when it effects the blood and bone marrow. As a general rule white blood cells are affected by leukemia. Leukemia dataset is composed of both images of blood smears from leukemia patients and non-leukemia patients. While earlier research has simply found leuke

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Kurniawan, Wandi Yusuf, and Putu Harry Gunawan. "Classification of Autism Spectrum Disorder Based on Facial Images Using the VGG19 Algorithm." Journal of Computing Science and Engineering 18, no. 1 (2024): 1–9. http://dx.doi.org/10.5626/jcse.2024.18.1.1.

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Meng, Chunzhu. "Weather forecast image classification based on KNN, CNN, and VGG19." Applied and Computational Engineering 76, no. 1 (2024): 167–76. http://dx.doi.org/10.54254/2755-2721/76/20240583.

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Image classification is an important technology in the field of computer vision, with the main objective of categorizing input images into different classes. It is used in many areas, such as weather forecasting. The significance of weather forecast image classification spans several areas, including enhancing weather forecast accuracy, aiding agriculture, optimizing the energy sector, ensuring transportation safety, and informing urban planning and construction. In this study, the author investigates the precision and efficiency of three machine learning algorithmsConvolutional Neural Network

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Fawwaz, Insidini, Yennimar Yennimar, N. P. Dharsinni, and Bayu Angga Wijaya. "The Optimization of CNN Algorithm Using Transfer Learning for Marine Fauna Classification." sinkron 8, no. 4 (2023): 2236–45. http://dx.doi.org/10.33395/sinkron.v8i4.12893.

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Marine fauna are all types of organisms that live in the marine environment. Marine fauna is also an important part of the marine ecosystem that has an important role in maintaining environmental balance. However, the survival of marine fauna is threatened due to activities carried out by humans, such as pollution, overfishing, industrial waste disposal into marine waters, plastic pollution and so on. Therefore, efforts are needed to monitor and protect marine fauna so that marine ecosystems can remain stable. One way to monitor marine fauna is by using classification technology. One of the te

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