Exploring the Potential of VGG-16 Architecture for Accurate Brain Tumor Detection Using Deep Learning

Prerepa Gayathri; Aiswarya Dhavileswarapu; Sufyan Ibrahim; Rahul Paul; Reena Gupta

doi:10.57159/gadl.jcmm.2.2.23056

Authors

Prerepa Gayathri Department of Electrical and Computer Engineering, NYU Tandon School of Engineering, NYU Tandon School of Engineering, New York University, New York, United States 11201; Department of Electronics and Communication, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India 576104 https://orcid.org/0000-0001-5661-742X
Aiswarya Dhavileswarapu Department of Electronics and Communication, GITAM University, Gandhi Nagar, Rushi Konda, Visakhapatnam, Andhra Pradesh, India 530045
Sufyan Ibrahim Neuro-Informatics Laboratory, Department of Neurological Surgery, Mayo Clinic, Rochester, Minnesota, USA 55905 https://orcid.org/0000-0001-9127-2738
Rahul Paul Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Harvard, USA 02115
Reena Gupta Department of Pharmacognosy, Institute of Pharmaceutical Research, GLA University, Mathura 281406, Uttar Pradesh, India 281406

DOI:

https://doi.org/10.57159/gadl.jcmm.2.2.23056

Keywords:

VGG-16 architecture, Brain tumor detection, Deep learning, Convolutional Neural Network (CNN), Accuracy

Abstract

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.

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