Relevant bibliographies by topics / VGG16 MODEL

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'VGG16 MODEL'

Author: Grafiati
Published: 11 September 2023
Last updated: 31 July 2025

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Journal articles on the topic "VGG16 MODEL"

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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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Dissertations / Theses on the topic "VGG16 MODEL"

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Albert, Florea George, and Filip Weilid. "Deep Learning Models for Human Activity Recognition." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20201.

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AMI Meeting Corpus (AMI) -databasen används för att undersöka igenkännande av gruppaktivitet. AMI Meeting Corpus (AMI) -databasen ger forskare fjärrstyrda möten och naturliga möten i en kontorsmiljö; mötescenario i ett fyra personers stort kontorsrum. För attuppnågruppaktivitetsigenkänninganvändesbildsekvenserfrånvideosoch2-dimensionella audiospektrogram från AMI-databasen. Bildsekvenserna är RGB-färgade bilder och ljudspektrogram har en färgkanal. Bildsekvenserna producerades i batcher så att temporala funktioner kunde utvärderas tillsammans med ljudspektrogrammen. Det har visats att inkluder
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GUPTA, RASHI. "IMAGE FORGERY DETECTION USING CNN MODEL." Thesis, 2022. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19175.

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Image forgery detection has become more relevant in the real world in recent years since it is so easy to change a particular image and share it throughout social media, which may quickly lead to fake news and fake rumors all over the world. These editing softwares have posed a significant challenge to image forensics in terms of proposing and implementing various methods and strategies for detecting image counterfeiting. There have been a variety of traditional approaches for forgery detection, but they all focus on simple feature extraction and are more specialized to the type of
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Book chapters on the topic "VGG16 MODEL"

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Gupta, Pranjal Raaj, Disha Sharma, and Nidhi Goel. "Image Forgery Detection by CNN and Pretrained VGG16 Model." In Advances in Intelligent Systems and Computing. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6887-6_13.

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Tiwari, Richa, and Rashi Agarwal. "LRP-Enhanced VGG16 Model for Interpretable Brain Tumor Classification." In Algorithms for Intelligent Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-3256-5_11.

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Anju, T. E., and S. Vimala. "Finetuned-VGG16 CNN Model for Tissue Classification of Colorectal Cancer." In Intelligent Sustainable Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1726-6_7.

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Chiu, Chuan-Feng, Chun-Hong Huang, Hsiang-Chieh Tseng, Pei-Jung Wu, Wei-Sheng Lin, and Hsiang-Min Peng. "The Design of Portrait Photography Guiding System Using VGG16 Model." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2395-2_27.

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Lincy, R. Babitha, and R. Gayathri. "Off-Line Tamil Handwritten Character Recognition Based on Convolutional Neural Network with VGG16 and VGG19 Model." In Advances in Automation, Signal Processing, Instrumentation, and Control. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8221-9_180.

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Singh, Aditya Vikram, Devshree Moghe, and K. Meenakshi. "Deepfake detection using fine-tuned VGG16 model: A transfer learning approach." In Challenges in Information, Communication and Computing Technology. CRC Press, 2024. http://dx.doi.org/10.1201/9781003559085-141.

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Raymahapatra, Prithwish, Avijit Kumar Chaudhuri, Sulekha Das, and Alex Khang. "Lung Cancer Prediction Using Convolutional Neural Network (CNN) with VGG16 Model." In Computer Vision and AI-Integrated IoT Technologies in the Medical Ecosystem. CRC Press, 2024. http://dx.doi.org/10.1201/9781003429609-15.

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Appavu, Narenthira Kumar, and C. Nelson Kennedy Babu. "COVID-19 prediction using deep learning VGG16 model from X-ray images." In Artificial Intelligence, Blockchain, Computing and Security Volume 1. CRC Press, 2023. http://dx.doi.org/10.1201/9781003393580-63.

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Vidya, D., Shivanand Rumma, and Mallikarjun Hangargi. "Apple Classification Based on MRI Images Using VGG16 Convolutional Deep Learning Model." In Proceedings of the First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022). Atlantis Press International BV, 2023. http://dx.doi.org/10.2991/978-94-6463-196-8_10.

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Ramya Manaswi, V., and B. Sankarababu. "A Flexible Accession on Brain Tumour Detection and Classification Using VGG16 Model." In Smart Intelligent Computing and Applications, Volume 1. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9669-5_21.

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Conference papers on the topic "VGG16 MODEL"

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Agsi, Beatrix Devanti, Slamet Riyadi, Nasy'an Taufiq Al Ghifari, Khairun Nisa, and Qodri Aziz. "Early Detection of Diabetes: Comparison of Inception, VGG16 and VGG19 Model." In 2025 International Conference on Smart Computing, IoT and Machine Learning (SIML). IEEE, 2025. https://doi.org/10.1109/siml65326.2025.11081116.

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Huang, Yintang, Han Chen, Junshen Hong, Zhihong Zhao, Zhenbang Kuang, and Youyu Zhou. "VGG16-AttnNet: a banana leaf lesion recognition model combining VGG16 and self-attention mechanism." In 2024 5th International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE). IEEE, 2024. http://dx.doi.org/10.1109/icbase63199.2024.10762417.

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Basamsetty, Pujitha, Samitha Kagitha, Prathyusha Nulu, and Vasanthi Pinniboyina. "Medicine recognition from coversheet using VGG16 model." In 2024 IEEE North Karnataka Subsection Flagship International Conference (NKCon). IEEE, 2024. https://doi.org/10.1109/nkcon62728.2024.10774855.

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Prasad, Ch Rajendra, J. Saikrishna, K. Akash, K. Sai Ganesh, Sreedhar Kollem, and Chakradhar Adupa. "Diabetic-Retinopathy Classification Using a Modified VGG16 Model." In 2024 International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET). IEEE, 2024. http://dx.doi.org/10.1109/acroset62108.2024.10743251.

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Mitra, Ankita, K. Sridar, S. Rathna, Rini Chowdhury, Prashant Kumar, and Glory E. "Optimizing Brain Tumor Mri Classification Using Modified Vgg16 Model." In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS). IEEE, 2024. http://dx.doi.org/10.1109/iacis61494.2024.10721808.

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Walia, Nikhil, Rishabh Sharma, Mukesh Kumar, Ankur Choudhary, and Vishal Jain. "Optimized VGG16 Model for Advanced Classification of Cotton Leaf Diseases." In 2024 4th International Conference on Intelligent Technologies (CONIT). IEEE, 2024. http://dx.doi.org/10.1109/conit61985.2024.10627057.

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Budiawan Zulfikar, Wildan, Aldy Rialdy Atmadja, Siti Lutfia Dwi Agustini, Yana Aditia Gerhana, and Dian Saadilah Maylawati. "VGG16 Model Architecture for Assessing Cucumber Ripeness Using Image Analysis." In 2024 12th International Conference on Cyber and IT Service Management (CITSM). IEEE, 2024. https://doi.org/10.1109/citsm64103.2024.10775803.

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Mishra, Anju, Gunjan Verma, Deepkiran Munjal, Divya Sharma, and Sanjeev Kumar. "Optimized VGG16 Model for Pneumonia Detection from Chest X-Rays." In 2025 3rd International Conference on Disruptive Technologies (ICDT). IEEE, 2025. https://doi.org/10.1109/icdt63985.2025.10986371.

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Achroufene, Achour, Lynda Raid, and Manel Tazibet. "Enhancing Face Detection: A Dual Model Approach with Modified VGG16 Layer." In 2024 International Conference on Information and Communication Technologies for Disaster Management (ICT-DM). IEEE, 2024. https://doi.org/10.1109/ict-dm62768.2024.10798958.

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Kaur, Arshleen, Rishabh Sharma, Saumitra Chattopadhyay, and Kireet Joshi. "Cotton Leaf Disease Classification Using Fine-Tuned VGG16 Deep Learning Model." In 2024 2nd World Conference on Communication & Computing (WCONF). IEEE, 2024. http://dx.doi.org/10.1109/wconf61366.2024.10692185.

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Reports on the topic "VGG16 MODEL"

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Cheng, DingXin. Development of the Roadway Pothole Management Program. Mineta Transportation Institute, 2024. http://dx.doi.org/10.31979/mti.2024.2306.

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Addressing the issue of potholes is a primary concern for maintaining urban infrastructure. The research team has developed a prototype pothole management program. The program includes a mobile application and two machine learning models. The mobile app enables users to upload images of potholes, report relevant information, and provide driving directions to the pothole location. With the help of this application, the user can seamlessly capture images of the potholes, record pertinent information, and submit the data for necessary action. The mobile application is an essential tool in the Pot
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