Automatic Classification for Accurate Representation of Defected Region of a Medical Image Segmentation

Abstract

Automatic or semi-automatic detection of the two-dimensional (2D) or three-dimensional (3D) image is included in the process of medical image segmentation that is performed automatically. In the fields of Intelligent Data Engineering and Automated Learning, image segmentation refers to the principles that are used to divide a digital image into a set of different sets of pixels (IDEAL). The representation of medical images must first be simplified before they can be transformed into a meaningful topic, which is one of the primary purposes of the segmentation process. Due to the significant degree of variation present in the images, segmentation is the job that presents the most challenge. The difficult difficulty in the medical image is segmenting the regions that are lacking edges, have no textural contrast, have a region of interest (ROI), and have a backdrop. “Intelligent Data Engineering and Automated Learning (IDEAL)” are two approaches that may help solve this problem. In response to this article, several automatic segmentation approaches have been presented, many of which have shown encouraging results. Because of the segmentation, medical professionals have an easier time diagnosing patients and making judgments. Following segmentation, a process known as feature descent must be extracted to extract the flawed features. With the aid of the ROI, the form of dimensionality reduction known as "feature descent" successfully displays the defective area of a medical image as a condensed version of the feature vector. The process of attribute reduction is referred to as the feature descent process. The feature is a graphical representation of the combined keywords. The issue with computer vision is solved by combining the processes of feature detection and descent. After the features have been extracted, the descent features will be included in the model construction process for more precise detection.