Detection and Classification of Brain Tumors using Magnetic Resonance Imaging and Artificial Neural Networks: A Novel Approach

This research introduces a unique method for identifying and categorizing brain tumours using MRI scans. There are two primary phases to the method suggested. In the first step, the MRI scans are analysed to look for signs of a brain tumor by extracting characteristics from the pictures and separating the tumor from the surrounding healthy tissue. The collected characteristics are then fed into an artificial neural network (ANN) for tumor classification. Several actions make up the initial phase of the suggested method. First, the MRI scans are preprocessed to increase the contrast between the tumor and healthy tissues, and then the tumor is segmented from the rest of the picture. At this point, you may use any number of segmentation strategies, including thresholding, region growth, and active contour models. After the tumor has been segmented, key properties such as texture, shape, and intensity are retrieved. In the second phase, the ANN receives these characteristics as input. Second, the retrieved characteristics are used to train the ANN to identify the tumor type. The artificial neural network (ANN) is a kind of machine learning algorithm that takes cues from how the human brain works. It’s built from several stacked nodes, each of which performs a different mathematical operation on the data being fed into it. The tumor’s anticipated class is represented by the ANN’s output from its deepest layer. The suggested technique offers significant benefits over the status quo when it comes to detecting and classifying brain tumors. Being totally automated, it removes human error and the inherent unpredictability in MRI picture analysis. It can also identify and categorise brain tumors, which is essential for helping doctors provide patients the best possible care. In sum, the suggested method has the potential to enhance the precision and efficacy with which brain tumors are diagnosed and treated.