Advanced Brain Tumor Diagnosis: MRI Image Classification with Deep Learning Technology

Currently several diseases are epidemic in Bangladesh, one of them is brain tumor. This disease usually affects people of any age. It usually spreads slowly in the human brain. Although initially it is small, as time goes by it does not look terrible, and the shape of the head becomes distorted. If the disease continues for a long time and without specific treatment, it often does not turn into cancer. Because the tumor affects the brain, it is called a brain tumor. Using machine learning and various image processing techniques, this paper proposes to automate brain tumor diagnosis. This dataset consists of images of brain tumor patients, non-brain tumors, and a total of 4 types of brain tumors. In this paper we have used the Customize CNN Algorithm, through which we have used the 7 architectures of CNN. The 7 custom CNN methods we used are VGG19, MobileNetV2, MobileNetV3, EfficientNet B0, EfficientNet B3, Inception V3 and DenseNet201. Here we can see that accuracy of VGG19 is 0.75, accuracy of MobileNetV2 is 0.88, accuracy of MobileNetV3 is 0.97, accuracy of EfficientNet B0 is 0.99, accuracy of EfficientNet B3 is 0. 99, Inception V3 is 0.86, and DenseNet201’s accuracy is 0.90. From all the methods it can be seen that EfficientNet B0 and EfficientNet B3 method has obtained the highest accuracy i.e., 0.99. The lowest accuracy i.e., 0.75 was obtained using the VGG19 model.