Classify Brain Tumors from MRI Images: Deep Learning-Based Approach

Brain tumors pose a significant health threat and cause severe damage to the body and its physiological functions. Traditional diagnostic methods for brain tumors involve expensive medical imaging scans and invasive surgical procedures, resulting in prolonged waiting times and recovery periods. Thus, we explore the potential of deep learning techniques and magnetic resonance imaging (MRI) for the diagnosis of brain tumors. With these technologies, we develop a diagnostic method that is faster, more accurate, and more reliable than current approaches. The proposed model employs preprocessing techniques and convolutional neural network (CNN) methods with the Adam optimizer. An average accuracy reaches 99.8% on the training set and 94.4% on the testing set. These results indicate that the classification of brain MRI is stable and reliable with the proposed method. This proposed approach outperforms four previous methods, demonstrating its superiority and potential for various applications in medical image analysis. In the future, improving overall performance and developing more advanced deep-learning models enables the medical community to diagnose diseases faster and more accurately.