Brain Tumor Detection Enhanced with Transfer Learning using SqueezeNet

Abstract

The study introduces the Brain Tumor Detection Transfer Learning Algorithm (BTDTLA), a novel model that employs transfer learning and a comprehensive dataset of brain images. The algorithm makes a significant breakthrough in the precise detection of brain tumors, particularly critical for cases requiring swift intervention. Development and testing of BTDTLA are conducted on MATLAB 2018. The evaluation metrics, including sensitivity, specificity, precision, accuracy, and the Matthews correlation coefficient, highlight the robust performance of BTDTLA, positioning it as a valuable tool for medical practitioners. This underscores the algorithm's potential to advance practices for early and accurate brain tumor detection. The study emphasizes BTDTLA's pivotal role in contributing to the field, underscoring its significance in enhancing medical practices related to brain tumor diagnosis.