MRI-Based Brain Tumor Classification Using Various Deep Learning Convolutional Networks and CNN

Abstract

"Yearly, brain tumors cause many fatalities and a significant portion of these victims come from rural regions. However, beginning brain tumor diagnosis technology is not as effective as anticipated. We thus set out to develop an accurate approach that would aid doctors in recognizing brain tumors. Even though there have been several types of research on this topic, we tried to develop a classification approach that is significantly more accurate and error-free and is trained using a sizable amount of authentic datasets rather than an enhanced data-modified version of the VGG-16 convolutional neural network architecture was used to analyze a dataset of 6328 MRI images that were categorized into three different types: Pituitary, Glioma, and Meningioma. The results were highly impressive, with the model achieving an overall accuracy of 99.5%. The precision rates for each type were also outstanding, with a precision rate of 99.4% for gliomas, 96.7% for meningiomas, and 100% for pituitaries. These results suggest that the modified VGG-16 architecture is highly effective in accurately classifying MRI images of the brain into these three distinct categories. Additionally, it outperformed various other current CNN designs and cutting-edge research in terms of outcomes."