Published Paper Details:

Brain tumors have recently emerged as one of the most critical issues for individuals suffering from severe headaches. However, most people are concerned that their headache is the result of a serious problem, such as a brain tumor, especially if they experience severe pain on a regular basis. In general, practically all brain tumors do not induce headaches since the brain has the ability to modulate discomfort. Some tumor cause more frequent headaches if the patient's brain contains a large tumor that puts pressure on nerves. A brain tumor is a sort of abnormal cell that develops in the human brain and is always classified as benign or malignant. If the tumor is detected in its early stages and therapy is initiated, the quality of life and life spam may improve. There is currently a high need for brain tumor diagnosis using various machine learning and deep learning techniques. With the advancement of artificial intelligence, deep learning models are being used to diagnose brain tumors using magnetic resonance imaging pictures. Magnetic resonance imaging (MRI) is a sort of scanning procedure that produces detailed images of the inner body by using powerful magnetic fields and radio waves. Deep learning methods such as convolutional neural network (CNN) models and VGG-16 architecture (developed from scratch) are used in this study to locate tumor regions in scanned brain pictures. We looked at brain MRI scans from 253 patients, 155 of which were tumors and 98 of whom were not. The research compares the outputs of the CNN model and the VGG-16 architecture used.