Brain MRI Analysis Using SqueezeNet for Accurate Medical Image Classification

Early diagnosis and efficient treatment of neurological illnesses need accurate brain Magnetic Resonance Imaging (MRI) classification. The suggested approach improves brain MRI analysis using SqueezeNet, a lightweight convolutional neural network. The goal is to achieve high accuracy with low computing complexity to meet medical imaging's rising need for scalable solutions. Improve classification performance, reduce false predictions, and adapt to different datasets. Its small design optimizes parameter utilization and accuracy, making SqueezeNet suited for resource-constrained systems. Fire modules and global pooling layers balance computational efficiency and classification accuracy in the model. Performance indicators like accuracy, sensitivity, and specificity reveal the model's robustness. A dependable and scalable solution to help healthcare practitioners quickly and accurately detect brain MRI abnormalities is the aim. This study advances medical imaging technology, especially in resource-constrained settings, by allowing accurate diagnosis. To improve SqueezeNet's performance and generalizability, transfer learning and data supplementation will be explored. The REMBRANDT database shows that SqueezeNet v1.2 performs best. The SqueezeNet v1.2 approach offers 97.4% sensitivity, 96.5% specificity, and 96.9% accuracy.