Biologically Inspired CNN Network for Brain Tumor Abnormalities Detection and Features Extraction from MRI Images

Image segmentation has become increasingly important in medical image analysis, but it does not remain easy to solve. Medical imaging is becoming more relevant since there is growing demand for automated, dependable, quick, and efficient diagnosis methods that result in better outcomes. With its billions of cells, the brain is one of the most complex organs in the human body. Males aged 20 to 39 are more likely than females in this age group to die from a brain tumor, which is the second leading cause of cancer-related mortality in men. Brain tumors can be unpleasant and cause various illnesses. Therefore, its early detection is critical as well as a trustworthy approach. It is critical to identify a benign or malignant tumor to diagnose it, and so tumor diagnosis is an essential step in the treatment process. The use of magnetic resonance imaging (MRI) in the detection of brain tumors is extremely beneficial. This paper will discuss a method that uses fundamental image processing techniques to provide tumor-specific information. These techniques include noise reduction, image sharpening, and morphological functions such as erosion and dilation to obtain the backdrop. This paper creates tumor images by removing the backdrop and their negatives from various photographs. Drawing the outline of the tumor and labeling it with a c-label provides us with information about the tumors that can better visualize cases when diagnosing them. The proposed method can determine the tumor size, shape, and location with an accuracy rate of 97.5%.