AEDAMIDL: An Enhanced and Discriminant Analysis of Medical Images using Deep Learning

In the field of healthcare, computer vision plays a very crucial role which is expected to expand exponentially in the coming decades. Computer vision solely focuses on understanding images and videos. This consists of tasks such as object detection, image classification, and segmentation. The process of visualizing the internal organs of the human body for clinical investigation and medical intrusion is called Medical imaging. Medical imaging has become a tool of paramount importance in clinical trials as it provides a swift diagnosis with visualization and quantitative assessment. Recent breakthroughs have significantly improved image classification and object detection models which are very useful for medical imaging. Medical Imaging technology can assist in detecting even the tiniest aberrations in the human body and for the last decades, computer-supported medical imaging applications have become a trustworthy help for physicians. It not only creates and analyzes images, but also helps doctors with their interpretation. However, a brain-related diagnosis necessitates extreme caution, and even the smallest error in judgment can be disastrous. This research engages with improving the diagnosis accuracy in identifying tumors in the brain. Several imaging technologies like CT scans and X-rays are available for investigating the brain but the MRI (Magnetic Resonance Imaging) has perpetually been the most reliable and safe for such diagnosis. Moreover, it provides a more desirable contrast in information about brain tissues. This further helps in building the model architecture which applies a comprehensive deep learning algorithm and Convolution Neural Networks (CNN) for classifying the tumor's possibility.