Automatic Brain Tumor Detection Classification Applying Multiple a Deep Learning Method

Automated detection of defects in medical imaging has emerged as a crucial domain within various diagnostic applications, especially with MRI regarding tumor detection. “While accuracy in the detection of tumors is important in providing vital information regarding tissues which have deviated from their normal state, it's even more important for effective treatment planning”. Currently, the diagnosis of tumors from brain MRI scans relies on human assessment, which is infeasible for large image data and error-prone based on judgments of humans. Thus, there is a pressing need for robust and efficient automated classification systems that can overcome these bottlenecks. The core idea of our project is the development of the most advanced tumor detection methods, which saves precious time for radiologists but at a very high accuracy and, thus, reduces mortality levels in patients. In our approach, we have used several CNNs - VGG19, ResNet50, and MobileNetV2, which we have applied through transfer learning for detecting tumors from brain MRIs. We also incorporate into our model an ensemble of these varied CNN architectures that leverages their strengths since the nature and diversity of tumors are intricate. In fact, the ensemble method is really intended to boost the wide-ranging accuracy and strength of the procedure in the detection of tumors. The proposed system utilizes deep learning techniques in combination with image classification to accurately and efficiently identify cancerous cells in brain MRI scans. After that, the web application, developed using Flask, will provide a user-friendly interface for uploading MRI images and then instantaneously return predictions to a doctor on the potential presence of a tumor.