MODIFIED SQUEEZENET WITH TRANSFER LEARNING FOR MRI-BASED BRAIN TUMOR DETECTION

The goal of this research is to create a reliable and effective deep learning framework for automatically identifying brain cancers from magnetic resonance imaging (MRI) scans. Brain tumors typically result from the unchecked proliferation of brain cells, some of which may develop into cancer. The ability of radiologists to accurately identify, segment, and classify tumor areas in MRI images is crucial, as it can result in inconsistent diagnoses. This study proposes a novel Transfer Learning-based Normalized Mixed Pooling SqueezeNet for Brain Tumor Detection (TLNMPS-BTD) architecture to improve tumor classification accuracy and reliability. A publicly accessible BraTS brain MRI dataset was used to assess the suggested framework, guaranteeing the repeatability and dependability of the experimental findings. In order to increase picture quality for further analysis, the suggested method first preprocesses MRI brain images using the Wiener Filtering Technique to eliminate noise and enhance image contrast. The region of interest is subsequently extracted by efficient segmentation using the Attention Mechanism-assisted SegNet (AMS). New Texture Computation on Local Gabor Transitional Pattern is a revolutionary texture-based technique for feature extraction. It increases the discriminative capability of retrieved features and improves low-resolution MRI pictures. The lightweight Normalized Mixed Pooling SqueezeNet (TLNMPS) model, which is based on Transfer Learning, receives these extracted features for tumor identification and classification. The proposed TLNMPS-BTD framework outperformed current state-of-the-art methods, achieving an accuracy of 96.8% and an F1-score of 96.7% at 90% training data.