Exploring the Potential of VGG-16 Architecture for Accurate Brain Tumor Detection Using Deep Learning
DOI:
https://doi.org/10.57159/gadl.jcmm.2.2.23056Keywords:
VGG-16 architecture, Brain tumor detection, Deep learning, Convolutional Neural Network (CNN), AccuracyAbstract
This study explores the potential of the VGG-16 architecture, a Convolutional Neural Network (CNN) model, for accurate brain tumor detection through deep learning. Utilizing a dataset consisting of 1655 brain MRI images with tumors and 1598 images with- out tumors, the VGG-16 model was fine-tuned and trained on this data. Initial training achieved an accuracy of 91%, which was improved to 94% after hyperparameter optimization. The model’s sensitivity, specificity, precision, recall, and F1 scores were strong, indicating its potential in accurately detecting brain tumors. The performance of the VGG-16 model was compared to several other techniques for brain tumor detection, including EasyDL, GoogLeNet, GrayNet, ImageNet, CNN, and a Multivariable Regression and Neural Network model. Although it did not achieve the highest accuracy, it outperformed GoogLeNet and ImageNet and demon- strated comparable accuracy to GrayNet and the Multivariable Regression and Neural Network. Its sensitivity and specificity suggest its potential in identifying tumors that other methods might miss, reinforcing its potential usefulness in medical applications. Never- theless, there is room for improvement. Future studies could collect and annotate larger datasets to improve model generalizability. Exploring other deep learning architectures and enhancing model interpretability could further boost its clinical relevance. Despite these challenges, this study demonstrates the untapped potential of the VGG-16 architecture in brain tumor detection and contributes to the growing body of research on applying deep learning in the medical field.
References
G. S. Tandel, M. Biswas, O. G. Kakde, A. Tiwari, H. S. Suri, M. Turk, J. Laird, C. Asare, A. A. Ankrah, N. N. Khanna, B. K. Madhusudhan, L. Saba, and J. S. Suri, “A Review on a Deep Learning Perspective in Brain Cancer Classification,” Cancers, vol. 11, p. 111, jan 2019.
Sheila K Singh, Ian D Clarke, Mizuhiko Terasaki, Victoria E Bonn, Cynthia Hawkins, Jeremy Squire, and Peter B Dirks, “Identification of a Cancer Stem Cell in Human Brain Tumors,” Cancer Research, vol. 63, pp. 5821–5828, 2003.
E. U. Haq, J. Huang, L. Kang, H. U. Haq, and T. Zhan, “Image-based state-of-the-art techniques for the identification and classification of brain diseases: a review,” Medical & Biological Engineering & Computing, vol. 58, pp. 2603–2620, nov 2020.
F. G. Kengne and G. Decaux, “CNS Manifestations of hyponatremia and its treatment,” Hyponatremia: Evaluation and Treat- ment, pp. 87–110, 2013.
R. J. Forsyth, J. Raper, and E. Todhunter, “Routine intracranial pressure monitoring in acute coma,” Cochrane Database of Systematic Reviews, vol. 2016, nov 2015.
A. Tiwari, S. Srivastava, and M. Pant, “Brain tumor segmentation and classification from magnetic resonance images: Review of selected methods from 2014 to 2019,” Pattern Recognition Letters, vol. 131, pp. 244–260, mar 2020.
A. K. Sharma, A. Nandal, A. Dhaka, and R. Dixit, “A survey on machine learning based brain retrieval algorithms in medical image analysis,” Health and Technology, vol. 10, pp. 1359–1373, nov 2020.
G. Mohan and M. M. Subashini, “MRI based medical image analysis: Survey on brain tumor grade classification,” Biomedical Signal Processing and Control, vol. 39, pp. 139–161, jan 2018.
M. W. Nadeem, M. A. Al Ghamdi, M. Hussain, M. A. Khan, K. M. Khan, S. H. Almotiri, and S. A. Butt, “Brain tumor analysis empowered with deep learning: A review, taxonomy, and future challenges,” Brain Sciences, vol. 10, no. 2, 2020.
X. Zhao and X. M. Zhao, “Deep learning of brain magnetic resonance images: A brief review,” Methods, vol. 192, pp. 131–140, 2021.
J. Bernal, K. Kushibar, D. S. Asfaw, S. Valverde, A. Oliver, R. Martí, and X. Lladó, “Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review,” Artificial Intelligence in Medicine, vol. 95, pp. 64–81, 2019.
S. Somasundaram and R. Gobinath, “Current Trends on Deep Learning Models for Brain Tumor Segmentation and Detection - A Review,” Proceedings of the International Conference on Machine Learning, Big Data, Cloud and Parallel Computing: Trends, Prespectives and Prospects, COMITCon 2019, pp. 217–221, 2019.
M. Tanveer, M. A. Ganaie, I. Beheshti, T. Goel, N. Ahmad, K.-T. Lai, K. Huang, Y.-D. Zhang, J. Del Ser, and C.-T. Lin, “Deep Learning for Brain Age Estimation: A Systematic Review,” 2022.
R. Kaur and A. Doegar, “Brain tumor segmentation using deep learning: taxonomy, survey and challenges,” in Brain Tumor MRI Image Segmentation Using Deep Learning Techniques, pp. 225–238, Elsevier, 2022.
M. Nazir, S. Shakil, and K. Khurshid, “Role of deep learning in brain tumor detection and classification (2015 to 2020): A review,” Computerized Medical Imaging and Graphics, vol. 91, p. 101940, jul 2021.
A. Santhosh, T. Saranya, S. Sundar, and S. Natarajan, “Deep Learning Techniques for Brain Tumor Diagnosis: A Review,”
Proceedings of the 4th International Conference on Microelectronics, Signals and Systems, ICMSS 2021, 2021.
R. Yang, Y. Du, X. Weng, Z. Chen, S. Wang, and X. Liu, “Automatic recognition of bladder tumours using deep learning technology and its clinical application,” The International Journal of Medical Robotics and Computer Assisted Surgery, vol. 17, apr 2021.
K. H. Cha, L. Hadjiiski, R. K. Samala, H.-P. Chan, E. M. Caoili, and R. H. Cohan, “Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets,” Medical Physics, vol. 43, pp. 1882–1896, mar 2016.
I. Lorencin, N. And¯elic´, J. Španjol, and Z. Car, “Using multi-layer perceptron with Laplacian edge detector for bladder cancer diagnosis,” Artificial Intelligence in Medicine, vol. 102, p. 101746, jan 2020.
S. A. Harmon, T. H. Sanford, G. T. Brown, C. Yang, S. Mehralivand, J. M. Jacob, V. A. Valera, J. H. Shih, P. K. Agarwal, P. L. Choyke, and B. Turkbey, “Multiresolution Application of Artificial Intelligence in Digital Pathology for Prediction of Positive Lymph Nodes From Primary Tumors in Bladder Cancer,” JCO Clinical Cancer Informatics, pp. 367–382, nov 2020.
X. Ma, L. M. Hadjiiski, J. Wei, H. Chan, K. H. Cha, R. H. Cohan, E. M. Caoili, R. Samala, C. Zhou, and Y. Lu, “U-Net based deep learning bladder segmentation in CT urography,” Medical Physics, vol. 46, pp. 1752–1765, apr 2019.
N. Coudray, P. S. Ocampo, T. Sakellaropoulos, N. Narula, M. Snuderl, D. Fenyö, A. L. Moreira, N. Razavian, and A. Tsirigos, “Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning,” Nature Medicine, vol. 24, pp. 1559–1567, oct 2018.
A. Cruz-Roa, H. Gilmore, A. Basavanhally, M. Feldman, S. Ganesan, N. N. Shih, J. Tomaszewski, F. A. González, and
A. Madabhushi, “Accurate and reproducible invasive breast cancer detection in whole-slide images: A Deep Learning approach for quantifying tumor extent,” Scientific Reports, vol. 7, p. 46450, apr 2017.
Y. Bar, I. Diamant, L. Wolf, S. Lieberman, E. Konen, and H. Greenspan, “Chest pathology detection using deep learning with non-medical training,” in 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), vol. 2015-July, pp. 294– 297, IEEE, apr 2015.
S. J. S. Gardezi, A. Elazab, B. Lei, and T. Wang, “Breast Cancer Detection and Diagnosis Using Mammographic Data: Systematic Review,” Journal of Medical Internet Research, vol. 21, p. e14464, jul 2019.
S. Sharma and K. Guleria, “Deep Learning Models for Image Classification: Comparison and Applications,” 2022 2nd In- ternational Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), pp. 1733–1738, apr 2022.
G. Yao, T. Lei, and J. Zhong, “A review of Convolutional-Neural-Network-based action recognition,” Pattern Recognition Letters, vol. 118, pp. 14–22, feb 2019.
J. Naranjo-Torres, M. Mora, R. Hernández-García, R. J. Barrientos, C. Fredes, and A. Valenzuela, “A Review of Convolutional Neural Network Applied to Fruit Image Processing,” Applied Sciences, vol. 10, p. 3443, may 2020.
S. Serte, A. Serener, and F. Al-Turjman, “Deep learning in medical imaging: A brief review,” Transactions on Emerging Telecommunications Technologies, vol. 33, oct 2022.
A. Ajit, K. Acharya, and A. Samanta, “A Review of Convolutional Neural Networks,” in 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), pp. 1–5, IEEE, feb 2020.
H.-C. Shin, H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers, “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,” IEEE Transactions on Medical Imaging, vol. 35, pp. 1285–1298, may 2016.
W. L. Alyoubi, W. M. Shalash, and M. F. Abulkhair, “Diabetic retinopathy detection through deep learning techniques: A review,” Informatics in Medicine Unlocked, vol. 20, p. 100377, 2020.
Z. Wang, Q. Liang, F. Duarte, F. Zhang, L. Charron, L. Johnsen, B. Cai, and C. Ratti, “Quantifying legibility of indoor spaces using Deep Convolutional Neural Networks: Case studies in train stations,” Building and Environment, vol. 160, p. 106099, aug 2019.
V. Sharma and R. N. Mir, “A comprehensive and systematic look up into deep learning based object detection techniques: A review,” Computer Science Review, vol. 38, p. 100301, nov 2020.
M. M. Kasar, D. Bhattacharyya, and T.-h. Kim, “Face Recognition Using Neural Network: A Review,” International Journal of Security and Its Applications, vol. 10, pp. 81–100, mar 2016.
X. Wu, D. Sahoo, and S. C. Hoi, “Recent advances in deep learning for object detection,” Neurocomputing, vol. 396, pp. 39–64, jul 2020.
M. Mcuba, A. Singh, R. A. Ikuesan, and H. Venter, “The Effect of Deep Learning Methods on Deepfake Audio Detection for Digital Investigation,” Procedia Computer Science, vol. 219, pp. 211–219, 2023.
M. M. Taye, “Theoretical Understanding of Convolutional Neural Network: Concepts, Architectures, Applications, Future Directions,” Computation, vol. 11, p. 52, mar 2023.
S. Batra, S. S. Malhi, G. Singh, and M. Mahajan, “A brief overview on deep learning methods for lung cancer detection using medical imaging,” Think India Journal, vol. 22, no. 30, pp. 1279–1288, 2019.
Downloads
Published
How to Cite
License
Copyright (c) 2023 Journal of Computers, Mechanical and Management
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The Journal of Computers, Mechanical and Management applies the CC Attribution- Non-Commercial 4.0 International License to its published articles. While retaining copyright ownership of the content, the journal permits activities such as downloading, reusing, reprinting, modifying, distributing, and copying of the articles, as long as the original authors and source are appropriately cited. Proper attribution is ensured by citing the original publication.
Accepted 2023-05-31
Published 2023-06-06
The journal is licensed under an 