Optimized Lightweight VGG16 for Effective Brain Tumor Detection and Classification

Abstract

The accurate detection and classification of brain tumors in medical imaging are crucial for effective diagnosis and treatment planning. In recent years, deep learning techniques have shown significant potential in enhancing the precision of such tasks. This study aims to develop a novel algorithm leveraging advanced deep learning methodologies to detect and extract the region of interest (ROI) of the affected area in brain tumor images. The proposed algorithm is designed to facilitate accurate identification and classification of brain tumors at various stages and types. To achieve this, a lightweight version of the VGG16 architecture has been employed, optimized to balance computational efficiency with high accuracy. This novel approach enhances the capability to segment the tumor area effectively, allowing for a detailed analysis of its characteristics. The integration of machine learning algorithms within the framework enables the classification of brain tumors into distinct categories, providing a comprehensive tool for medical professionals. The proposed lightweight VGG16 model achieves a commendable accuracy of 96%, highlighting its effectiveness in brain tumor classification tasks. By focusing on the extraction of the ROI and subsequent classification, this algorithm provides a robust solution for early and accurate brain tumor detection. The results of this study demonstrate the potential of advanced deep learning models in medical imaging, particularly in enhancing the accuracy and efficiency of brain tumor diagnosis. This work contributes to the ongoing efforts to improve diagnostic tools in the field of medical imaging, ultimately supporting better patient outcomes.