An Enhanced Brain Tumor Detection Scheme using a Hybrid Deep Learning Model

Brain tumor detection using machine learning techniques seems to be a promising approach to improve the accuracy and efficiency of the diagnosis process. This research proposes a hybrid brain tumor detection scheme using convolutional neural networks (CNN) and BAT algorithms. This method involves the use of medical imaging, such as magnetic resonance imaging (MRI) or computed tomography (CT) scans, to detect and classify brain tumors. The removal of the past and its negative repercussions from diverse pixy units causes age extraction. By mapping the contour and c-label of the tumor and its limitations, we can acquire information about the tumor that could aid in diagnosis. This approach may be used to identify the tumor's size, shape, and function. The clinical team and the patient can detect the significance of the tumor by using different color labeling different levels of elevation. The clinical workforce can send data to a graphical user interface for the shape and limitations of the tumor. The CNN is trained to identify patterns and features in the images that are indicative of a tumor, while the bat algorithm is used to optimize the parameters of the CNN for improved performance. This approach has the potential to increase the accuracy and speed of brain tumor diagnosis.