Convolutional neural network based brain tumor detection using image segmentation and classification

Deep learning’s application to machine learning has seen significant growth in recent years. One of the greatest difficulties now confronting AI systems is the integration of visual data with machine learning in the field of medical diagnostics. Brain tumours, the result of unchecked cell proliferation, are a particularly lethal kind of illness. If painful brain tumours aren’t treated properly, they may spread and cause other problems. Cancer treatment relies heavily on an accurate diagnosis. In order to determine whether a tumour is benign or malignant, accurate identification is required. More individuals are diagnosed with cancer now than ever before in part because physicians lack the knowledge to effectively treat tumours at an early stage. In this research, we classify brain MRI and PET scans into tumour and nontumor categories using a convolutional neural network (CNN) technique, augmented by Data Augmentation (DA) and Image Processing (IP) with Image Acquisition (IA). Although we only used a tiny dataset in our experiment, the high level of accuracy (96%) it achieved vouched for the efficiency and minimal complexity of our model. Our approach requires much less computational resources to achieve comparable levels of accuracy as compared to previous pre-trained models.