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Relevant bibliographies by topics / The VGG-16 convolutional

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Academic literature on the topic 'The VGG-16 convolutional'

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

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Journal articles on the topic "The VGG-16 convolutional"

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Haay, Happy Alyzhya, Suryasatriya Trihandaru, and Bambang Susanto. "INTRODUCTION OF PAPUAN AND PAPUA NEW GUINEAN FACE PAINTING USING A CONVOLUTIONAL NEURAL NETWORK." BAREKENG: Jurnal Ilmu Matematika dan Terapan 17, no. 1 (2023): 0211–24. http://dx.doi.org/10.30598/barekengvol17iss1pp0211-0224.

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In this research, the face painting recognition of Papua and Papua New Guinea was identified using the Convolutional Neural Network (CNN). This CNN method is one of the deep learning that is very well known and widely used in face recognition. The best training process model is obtained using the CNN architecture, namely ResNet-50, VGG-16, and VGG-19. The results obtained from the training model obtained an accuracy of 80.57% for the ResNet-50 model, 100% for the VGG-16 model, and 99.57% for the VGG-19 model. After the training process, predictions were continued using architectural models with test data. The prediction results obtained show that the accuracy of the ResNet-50 model is 0.70, the VGG-16 model is 0.82, and the VGG-19 model is 0.83. It means that the CNN architectural model that has the best performance in making predictions in identifying the recognition of Papua and Papua New Guinea's face painting is the VGG-19 model because the accuracy value obtained is 0.83.

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Amjad Khan, Dr. "Performance of VGG-16 Convolutional Neural Network Model Based Lung Cancer Classification on Computed Tomography." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47387.

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Abstract— The robust nodule detection challenge in lung cancer identification has become difficult due to the variability of lung nodules and the complexity of the surrounding environment. Early detection of lung nodules is crucial for lung cancer survival and is an effective strategy to reduce patient mortality. The proposed method for identifying lung nodules from CT images utilizes VGG-16 convolutional neural networks, eliminating the need for manual feature extraction, as per previous feedback. The network is fed with raw lung CT images from publicly available LIDC-IDRI dataset. The VGG-16 convolutional neural network successfully classified lung CT images into benign and malignant categories, achieving 86% accuracy and reducing false positive rates. Keywords— VGG-16, Lung Cancer, Computed Tomography, Classification

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Saleh, Yaser, and Muhanna Muhanna. "Archeological Sites Classification Through Partial Imaging and Convolutional Neural Networks." International Journal of Online and Biomedical Engineering (iJOE) 19, no. 07 (2023): 26–38. http://dx.doi.org/10.3991/ijoe.v19i07.39045.

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In this paper, a novel approach for classifying archeological sites using publicly available images through the use of Convolutional Neural Networks (CNNs) is presented. To surmount the problem of having a limited amount of data to use in training and testing the CNNs, our approach employs the technique of fine tuning. We conducted an experiment with four popular CNN architectures: VGG-16, VGG-19, ResNet50, and InceptionV3. The results show that our models achieved an impressive accuracy of up to 98% using the VGG-16 and InceptionV3 models and up to 97% using the ResNet50 model, while the VGG-19 model produced results with an accuracy of 95%. The results of this study demonstrate the effectiveness of our proposed approach in classifying archeological sites using publicly available images and highlight the potential of deep learning techniques for archeological site classification.

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Agus, Minarno Eko, Sasongko Yoni Bagas, Munarko Yuda, Nugroho Adi Hanung, and Zaidah Ibrahim. "Convolutional Neural Network featuring VGG-16 Model for Glioma Classification." JOIV : International Journal on Informatics Visualization 6, no. 3 (2022): 660. http://dx.doi.org/10.30630/joiv.6.3.1230.

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Magnetic Resonance Imaging (MRI) is a body sensing technique that can produce detailed images of the condition of organs and tissues. Specifically related to brain tumors, the resulting images can be analyzed using image detection techniques so that tumor stages can be classified automatically. Detection of brain tumors requires a high level of accuracy because it is related to the effectiveness of medical actions and patient safety. So far, the Convolutional Neural Network (CNN) or its combination with GA has given good results. For this reason, in this study, we used a similar method but with a variant of the VGG-16 architecture. VGG-16 variant adds 16 layers by modifying the dropout layer (using softmax activation) to reduce overfitting and avoid using a lot of hyper-parameters. We also experimented with using augmentation techniques to anticipate data limitations. Experiment using data The Cancer Imaging Archive (TCIA) - The Repository of Molecular Brain Neoplasia Data (REMBRANDT) contains MRI images of 130 patients with different ailments, grades, races, and ages with 520 images. The tumor type was Glioma, and the images were divided into grades II, III, and IV, with the composition of 226, 101, and 193 images, respectively. The data is divided by 68% and 32% for training and testing purposes. We found that VGG-16 was more effective for brain tumor image classification, with an accuracy of up to 100%.

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Rashid, Iqbal, and Basit Abdul. "Enhancing Vehicle Classification Accuracy: A Convolutional Neural Network (CNN) Based Model." LC International Journal of STEM 5, no. 1 (2024): 1–11. https://doi.org/10.5281/zenodo.11074270.

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Using the convolutional neural network (CNN) fine-tuned method, this article introduces a vehicle categorization system. The system's goal is to properly categorize popular vehicle types in the domestic market, which will help with traffic control, monitoring, and traffic accident prevention. The efficacy of VGG-16 and Inception V3 architectures is demonstrated by their evaluation of a real-world dataset consisting of 2000 photos of vehicles. While VGG-16 attains an accuracy of 99.11%, Inception V3 reaches an accuracy of 96.43%. In terms of overall accuracy, VGG-16 outperforms Inception V3, highlighting the importance of architectural decisions in achieving accurate vehicle classification. The suggested technique significantly improves computer vision applications in the domain of vehicle classification, making valuable contributions to traffic management and accident prevention efforts.

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G. P .S .Varma, K. Srinivas ,. P. V. G. D. Prasad Reddy ,. "Deciphering the Superiority of VGG-16 in Transfer Learning for Breakthrough Precision in Human Pose." Tuijin Jishu/Journal of Propulsion Technology 44, no. 6 (2023): 2992–3000. http://dx.doi.org/10.52783/tjjpt.v44.i6.3831.

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In the present research, we investigate how well human position estimation can be accomplished by combining transfer learning (TL) with the VGG-16 deep convolutional neural network (DCNN). TL is a logical approach to take advantage of the streamlined training process and higher precision of cutting-edge models. We provide an experimental setup for comparing VGG-16's results with those of more conventional approaches for human posture assessment. We also detail an experiment conducted to assess TL's performance on VGG-16. We found that VGG-16 is capable of producing reliable estimates of human postures, and that the network's feature representation significantly enhanced the model's performance with TL. Our experimental results further show that VGG-16 outperformed conventional approaches, especially when dealing with complicated data. In addition, we discovered that TL with VGG-16 considerably improved the accuracy of posture estimation tasks, suggesting that the model may be used to speed up a variety of tasks related to stance estimation. Our findings suggest that transfer learning using VGG-16 might be a useful and time-saving method for human posture estimation.

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Riyadi, Sugeng, Doughlas Pardede, and Raja Nasrul Fuad. "Klasifikasi Kategori Cuaca Berdasarkan Citra Menggunakan VGG-16." Data Sciences Indonesia (DSI) 4, no. 1 (2024): 91–98. https://doi.org/10.47709/dsi.v4i1.4664.

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Penelitian ini bertujuan untuk mengklasifikasikan kondisi cuaca seperti Berawan, Cerah, dan Terbit, menggunakan citra digital dengan pendekatan otomatis berbasis Convolutional Neural Network (CNN). Arsitektur VGG-16 dipilih karena kemampuannya dalam mengekstraksi fitur detail melalui lapisan konvolusional bertingkat. Dataset yang digunakan berisi 910 citra, dibagi menjadi tiga kategori, dan diolah menggunakan VGG-16 untuk menghasilkan vektor fitur berdimensi 4096. Klasifikasi dilakukan dengan jaringan saraf tiruan yang memiliki tiga lapisan tersembunyi, dan evaluasi model menggunakan metode 10-fold cross-validation. Metrik yang digunakan untuk menilai kinerja model adalah akurasi, presisi, dan recall. Hasil penelitian menunjukkan bahwa VGG-16 mampu mengklasifikasikan citra dengan akurasi sebesar 96,48%, dengan performa terbaik pada kelas Berawan, Cerah, dan Terbit, yaitu masing-masing 96%, 95,5%, dan 97,2%. Meskipun model menunjukkan akurasi tinggi, tantangan masih ada dalam membedakan citra dengan fitur visual yang serupa, seperti intensitas cahaya dan formasi awan. Kesimpulannya, VGG-16 efektif dalam klasifikasi kondisi cuaca berbasis citra digital, namun memerlukan pengembangan lebih lanjut untuk mengatasi kesalahan klasifikasi akibat kemiripan visual antara kategori cuaca

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Geng, Zhui. "Improve Unsupervised Machine Learning Model on Fruits by Using VGG-16." Highlights in Science, Engineering and Technology 85 (March 13, 2024): 1135–40. http://dx.doi.org/10.54097/39xpbb68.

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The demand for efficient fruit classification has risen with supply chain complexities. For the sake of saving the labor force and improving efficiency, applying machine learning algorithms on fruit categorization to multiple stages in the factory is a feasible solution. The paper briefly delivers the rationale of algorithm VGG-16 and validates its advantage in accuracy by comparing it with other convolutional neural network models. Traditional convolutional network reaches its peak accuracy which is around 40% after 45 training epochs. VGG-16 results with a 97% percent accuracy on the classification of more than 20 kinds of fruits only after 6 epochs and can be improved further by enriching and augmenting the dataset. However, although VGG-16 only requires a small number of epochs, the weighted parameter for each layer increases dramatically and increases the running time. Future researchers should focus on optimizing the algorithm to make it more feasible in the industry. Also, remote computing might be a solution for large computational requirements.

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MOH MARIO SUBAGIO, Mulyani Satya Bhakti, Achmad Yusuf Yulestiono, and Anggraini Puspita Sari. "Perbandingan Kinerja Metode Convolutional Neural Network (CNN) dan VGG-16 dalam Klasifikasi Rambu Lalu Lintas." Jurnal Mahasiswa Teknik Informatika 3, no. 2 (2024): 79–87. http://dx.doi.org/10.35473/jamastika.v3i2.3361.

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Penelitian ini membandingkan kinerja metode Convolutional Neural Network (CNN) dan VGG-16 dalam klasifikasi rambu lalu lintas menggunakan dataset gambar yang telah diproses sebelumnya. CNN yang digunakan melibatkan beberapa lapisan konvolusi, pooling, dropout, dense, serta penerapan data augmentasi untuk meningkatkan performa model. Hasil penelitian menunjukkan bahwa CNN sederhana mampu menghasilkan akurasi yang lebih baik dalam klasifikasi rambu lalu lintas dibandingkan dengan VGG-16. Model CNN terdiri dari lapisan-lapisan yang lebih sederhana namun efektif dalam mengekstraksi fitur dan mengurangi dimensi data, sehingga mengurangi kompleksitas komputasi dan mencegah overfitting. VGG-16, yang merupakan salah satu arsitektur CNN yang lebih kompleks dan mendalam, memerlukan sumber daya komputasi yang lebih besar serta waktu pelatihan yang lebih lama. Meskipun VGG-16 dilatih dengan fine-tuning pada beberapa lapisan terakhir untuk menyesuaikan model dengan dataset rambu lalu lintas, hasil eksperimen menunjukkan bahwa VGG-16 masih memerlukan lebih banyak waktu dan sumber daya dibandingkan dengan CNN sederhana. Hasil penelitian ini menyimpulkan bahwa CNN sederhana tidak hanya efisien dan efektif untuk aplikasi dengan keterbatasan sumber daya tetapi juga mampu memberikan akurasi yang lebih tinggi dibandingkan VGG-16. Keunggulan CNN sederhana terletak pada efisiensi komputasi dan kemampuannya untuk dioptimalkan lebih lanjut, termasuk penerapan teknik transfer learning untuk meningkatkan performa model tanpa perlu pelatihan ulang dari awal. Dengan demikian, CNN sederhana menjadi pilihan yang lebih ideal untuk aplikasi klasifikasi rambu lalu lintas, terutama dalam konteks yang memerlukan efisiensi waktu dan sumber komputasi. Penelitian ini membuka peluang untuk eksplorasi lebih lanjut dalam penggunaan teknik optimisasi dan transfer learning guna meningkatkan kinerja model dalam berbagai aplikasi klasifikasi gambar. Kata Kunci: rambu lalu lintas, VGG16, convolutional neural network (CNN)

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Moumen, Rajae, Raddouane Chiheb, and Rdouan Faizi. "Real-time Arabic scene text detection using fully convolutional neural networks." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 2 (2021): 1634. http://dx.doi.org/10.11591/ijece.v11i2.pp1634-1640.

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The aim of this research is to propose a fully convolutional approach to address the problem of real-time scene text detection for Arabic language. Text detection is performed using a two-steps multi-scale approach. The first step uses light-weighted fully convolutional network: TextBlockDetector FCN, an adaptation of VGG-16 to eliminate non-textual elements, localize wide scale text and give text scale estimation. The second step determines narrow scale range of text using fully convolutional network for maximum performance. To evaluate the system, we confront the results of the framework to the results obtained with single VGG-16 fully deployed for text detection in one-shot; in addition to previous results in the state-of-the-art. For training and testing, we initiate a dataset of 575 images manually processed along with data augmentation to enrich training process. The system scores a precision of 0.651 vs 0.64 in the state-of-the-art and a FPS of 24.3 vs 31.7 for a VGG-16 fully deployed.

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Book chapters on the topic "The VGG-16 convolutional"

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Rawat, Jyoti, Doina Logofătu, and Sruthi Chiramel. "Factors Affecting Accuracy of Convolutional Neural Network Using VGG-16." In Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48791-1_19.

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Asiya, Afreen, Gandam Rohith, Guluri Manitha Goud, Vallabhu Tharun, and Kukatla Abhiram. "Detection of Tampered Images with Convolutional Neural Networks (VGG-16)." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-7616-0_21.

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He, Zhexin, and Huan Zhang. "A Method of Concrete Surface Crack Detection Using an Improved Convolutional Neural Network (CNN) Model." In Novel Technology and Whole-Process Management in Prefabricated Building. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5108-2_36.

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AbstractThis essay spotlights concrete crack detection in infrastructure maintenance, highlighting its importance for structural integrity, cost-effectiveness, and eco-consciousness. It delves into various detection methods and introduces an improved VGG-16-based deep learning model with batch normalization, P-ReLU activation, and Adam optimization for better training outcomes. Through experiments on the MendeleyData-CrackDetection dataset, the enhanced model outperforms the original. This study underscores the significance of hyperparameter optimization and algorithm choice in deep learning.

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Jadhav, Rahul Namdeo, G. Sudhagar, and Sharad Sarjerao Jagtap. "‘Convolutional neural network architecture for tumor segmentation identification leveraging the VGG-16 model." In Recent Trends in VLSI and Semiconductor Packaging. CRC Press, 2025. https://doi.org/10.1201/9781003616399-61.

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Athish, M. J., S. Sowmya, E. Lakshaya, S. Preetha, S. U. Rishiya Shankar, and K. S. Sendhil Kumar. "Sign Language Recognition Using Convolutional Neural Network Based on VGG-16 Deep Learning Approach." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-6106-7_31.

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Altememe, Maha Sabri, and Wael Mahdi Brich. "Dynamic Hand Gesture Recognition Using a One-Dimensional Convolutional Neural Network and VGG-16." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-3361-6_16.

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Suedumrong, Chaichana, Komgrit Leksakul, Pranprach Wattana, and Poti Chaopaisarn. "Application of Deep Convolutional Neural Networks VGG-16 and GoogLeNet for Level Diabetic Retinopathy Detection." In Lecture Notes in Networks and Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89880-9_5.

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Hasan, Amal O., and Zakariya A. Oraibi. "Lung and Colon Cancer Classification Based on a Hybrid Deep Convolutional Neural Networks of Xception, VGG-16, and VGG-19 Using Histopathological Images." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-85902-1_30.

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Singh, Inderpreet, Sunil Kr Singh, Sudhakar Kumar, and Kriti Aggarwal. "Dropout-VGG Based Convolutional Neural Network for Traffic Sign Categorization." In Lecture Notes on Data Engineering and Communications Technologies. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9416-5_18.

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Singh, Jagendra, Vinish Kumar, Kotha Sinduja, Pongkit Ekvitayavetchanukul, Atul Kumar Agnihotri, and Hazra Imran. "Enhancing Heart Disease Diagnosis Through Particle Swarm Optimization and Ensemble Deep Learning Models." In Advances in Computer and Electrical Engineering. IGI Global, 2024. https://doi.org/10.4018/979-8-3693-6834-3.ch010.

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The present research focused on combining Particle Swarm Optimization (PSO) based hybrid deep learning models to classify heart disease images and patient sequences. This study employs Convolutional Neural Networks (CNNs), including VGG 16, VGG 19 and ResNet 50, as well as Recurrent Neu-ral Networks (RNNs), whereby their performance is optimized by PSO to im-prove the accuracy in diagnosing heart disease from CT images together with associated medical history. The models experienced a significant increase in classification performance, using manual hyperparameters tuning by PSO. The combined algorithm PSO with VGG 19 and the RNN model performed best, achieving a precision of 97.78% and becoming the highest recall on testing. The model that we propose uses the modern feature extraction of VGG 19 and an RNN to take into consideration the sequential nature of data, making it very accurate while keeping loss minimal. PSO with VGG 16 and RNN model is also another robust performance with an accuracy of 94.5%.

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Conference papers on the topic "The VGG-16 convolutional"

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D, Surendren, and Sumitha J. "Skin Cancer Detection using Convolutional Neural Network Models: VGG-16, VGG-19, ResNet and DenseNet." In 2024 5th International Conference on Data Intelligence and Cognitive Informatics (ICDICI). IEEE, 2024. https://doi.org/10.1109/icdici62993.2024.10810998.

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Anthony, Celia, and Savan Patel. "Deep Convolutional Q-Learning system and VGG-16 steganalysis detector for image Steganography." In 2024 7th International Conference on Circuit Power and Computing Technologies (ICCPCT). IEEE, 2024. http://dx.doi.org/10.1109/iccpct61902.2024.10673401.

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Ramadhan, Iqbal Putra, Dian Sa'Adillah Maylawati, Diena Rauda Ramdania, Agung Wahana, Cepy Slamet, and Rifqi Syamsul Fuadi. "Convolutional Neural Network with VGG-16 Architecture for Object Image Classification in Arabic Language." In 2024 10th International Conference on Wireless and Telematics (ICWT). IEEE, 2024. http://dx.doi.org/10.1109/icwt62080.2024.10674728.

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Sanni, Septian Luthfia, Khadijah, Budi Warsito, Prajanto Wahyu Adi, Adhe Setya Pramayoga, and Rismiyati. "Classification of Tea Leaf Clone Using Custom Convolutional Neural Network Based on VGG-16." In 2024 7th International Conference on Informatics and Computational Sciences (ICICoS). IEEE, 2024. http://dx.doi.org/10.1109/icicos62600.2024.10636855.

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Khannum, Ayesha, Dhanesha R, and Kantharaj Y. "Classification of Downy Mildew Disease in Watermelon Plant Leaf using VGG-16 Convolutional Neural Network." In 2024 4th International Conference on Mobile Networks and Wireless Communications (ICMNWC). IEEE, 2024. https://doi.org/10.1109/icmnwc63764.2024.10872300.

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Dong, Zujian, Hongyi Zhu, and Yiheng Li. "An Innovative Approach to Cross-Age Face Recognition: Combining Deformable Convolutional Networks with VGG-16 Network." In 2024 5th International Conference on Computers and Artificial Intelligence Technology (CAIT). IEEE, 2024. https://doi.org/10.1109/cait64506.2024.10963038.

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Rajaram, Renugadevi, Midhunchakkaravarthy, Janani Selvam, and Ashok Vajravelu. "Enhancing Lung Cancer Detection using VGG-16: Leveraging Deep Convolutional Neural Networks for Accurate Medical Image Analysis." In 2024 2nd International Conference on Recent Trends in Microelectronics, Automation, Computing and Communications Systems (ICMACC). IEEE, 2024. https://doi.org/10.1109/icmacc62921.2024.10894308.

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Kaizer, Wesley, and Otavio Correa. "A Machine Vision Case Study of U-Net Networks for Superficial Corrosion and Dirt Image Segmentation on Industrial Coated Steel Structures." In CONFERENCE 2022. AMPP, 2022. https://doi.org/10.5006/c2022-17995.

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Abstract Corrosion detection in industrial assets and components is an important broad problem in the industries since it allows the temporal tracking of possible issues and the execution of preventive maintenance actions, such as protective coating. However, solving this problem using modern machine learning methods usually demands a careful design of artificial intelligence tools, such as neural networks, high computational resources for training and inference, and a large and adequate dataset. In this work we investigate the application of deep convolutional neural networks to the problem of image semantic segmentation of superficial corrosion and dirt present in mining industrial assets, using a set of images collected in place by corrosion inspectors and manually labeled by a data team. We compare two networks based on the popular U-Net model, in which one of them uses the transferred features from a pre-trained VGG-16 image classification model. Our main contribution is to provide insights about the application of deep neural networks in this particular domain, mostly regarding the size and quality of the constructed dataset, the existing computational resources constraints, and the observed benefits of using a pre-trained model, as well as discuss some preliminary segmentation results obtained. Our main results show the practical usefulness of transfer learning approaches, which presents significantly better results in our dataset, and also highlight the importance of constructing a dataset with appropriate size and sample quality.

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Srivastava, Anamika, Anuj Gupta, Deepti Kaushik, Neha Gupta, Shikha Chadha, and Rosey Chauhan. "Automated Identification of Pneumonia Employing Convolution Neural Networks and the VGG-16 Model." In 2024 International Conference on Computing, Sciences and Communications (ICCSC). IEEE, 2024. https://doi.org/10.1109/iccsc62048.2024.10830428.

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Manansala, Angelica J., and Engr Charmaine C. Paglinawan. "Classification of Coffea Liberica Quality Using Convolution Neural Networks (Slim-CNN, YOLOv5, and VGG-16)." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10723931.

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