Harnessing Machine Learning for Enhanced Diagnosis and Treatment of Brain Cancer

Detecting brain cancer is a crucial aspect of medical imaging, where the accuracy and timeliness of diagnosis can profoundly affect patient outcomes. This study introduces a deep learning (DL) approach, utilizing the VGG16 architecture, to identify brain tumors. The model was developed and tested using a dataset of MRI images, with the primary focus on distinguishing between malignant and benign tissues. The model’s training was optimized using Stochastic Gradient Descent (SGD), which played a key role in achieving efficient learning and model convergence. Over the course of 30 training epochs, the model showed consistent improvements in accuracy, ultimately reaching a peak validation accuracy of 98%. The confusion matrix provided further validation of the model’s performance, reflecting a well-balanced classification with minimal occurrences of false positives and false negatives. This study underscores the effectiveness of combining VGG16 with SGD in brain tumor detection. The model’s high accuracy and low error rates highlight its robustness, suggesting strong potential for practical use in clinical settings. Such a model could be highly beneficial for radiologists, offering an automated tool that supports early and precise brain cancer diagnosis, thereby contributing to enhanced patient care.