Automated Brain Tumor Classification System using Convolutional Neural Networks from MRI Images

Recent advances in machine learning have employed deep learning to do several tasks. Deep learning has been used in the health sector to solve complex problems that require human intelligence. Without timely medical attention, the prognosis for patients with brain tumors is dismal. Radiologists are responsible for classifying tumors in radiographic images, which is a complex and time-consuming process that relies solely on their expertise. Modern radiology diagnosis, such as magnetic resonance (MR) scans, is largely subjective, putting patients at risk of damage. Use of Artificial Intelligence (AI) technology in order to avoid making mistakes when diagnosing is important to success. An automated approach for classifying different brain tumor classes in patients using magnetic resonance imaging (MRI) was suggested in this research, which focused on merging deep learning and radionics. We performed our work on three unique datasets with several classes. The proposed technique makes use of a convolutional neural network (CNN) as our deep learning model with the K-fold cross-validation concept in order to perform both binary and multiclass classification on our magnetic resonance imaging (MR) data. We took advantage of the power of CNN architecture in medical imaging. The model was trained and tested on random folded images from the dataset and was able to get an accuracy rate of 100%, 99.86%, and 100% in the corresponding dataset respectively, those are utterly remarkable, to put it mildly.