Classification of Brain Tumor Images using Transfer Learning

Brain tumors provide a substantial risk to human well-being because of their complex nature and the challenges linked to identifying and treating them at an early stage. Precise diagnosis is crucial for efficient intervention, although existing approaches often encounter constraints in terms of both accuracy and efficiency. This paper presents a new and innovative deep learning (DL) method that utilizes the Efficient Net family to enhance the classification and detection of brain tumors. The investigations show that EfficientNetB0 attained accuracy rates of 97.14%, 97.76%, 97.07%, 97.69%, and 97.07% across several criteria. The focal point of our study is the EfficientNetB3 model, which demonstrated outstanding performance, with an accuracy rate of 97.69%. Our method demonstrates superior effectiveness compared to numerous current state-of-the-art procedures. Our highly accurate DL model ensures enhanced diagnostic reliability and speed in clinical settings, facilitating earlier and more successful treatment solutions. The results of our study indicate a substantial opportunity to enhance patient outcomes in the diagnosis of brain tumors