Revolutionizing Brain Tumor Diagnosis: Harnessing Convolutional Neural Networks for Enhanced Prediction and Classification

Diagnosing brain tumors correctly and on time is the only way of effectively planning suitable treatment. Advances made recently in the field of medical imaging and artificial intelligence are leading to better methods of diagnosis. This paper is going to explore how Convolutional Neural Networks (CNNs) can be used to predict and classify tumors in the brain. The challenges of diagnosing brain tumors, the importance of CNNs in medical image processing, a detailed survey about recent studies and techniques used in this area would also be covered. Moreover, we propose an innovative CNN architecture designed for brain tumor detection by using modern developments in deep learning.That said, a two stage analysis methodology was followed with a total focus towards prevalent challenges within Image Restoration and Image Enhancement. As such, this study introduces new techniques that address these issues effectively.This study proposes an approach utilizing the power of Convolutional Neural Networks (CNNs). The novel CNN classification technique is applied, leveraging the Python and TensorFlow environment.The findings underscore the potential of this approach in revolutionizing brain tumor detection. By providing detailed insights into the strengths and limitations of the proposed model, this research contributes significantly to the field.The paper concludes by emphasizing the transformative impact of this research, opening avenues for further exploration and innovation in this critical domain.