Image Segmentation and classification Hepatitis viral infection in human blood smear with a hybrid algorithm combining Naive Bayes Classifier

Abstract

The field of medical informatics incorporates two types of medical data: biological records and imaging data. Pixels that correspond to a part of a physical entity and are created by imaging modalities make up medical image records. Exploration of medical image data techniques is difficult in terms of determining their importance in terms of insight, analysis, and diagnosis of a particular illness. Image processing is a significant problem in image processing activities and plays a vital part in computer-aided diagnosis. This task was about using tools and techniques to manipulate image processing results, pattern recognition results, and classification methods, and then validating the image classification results against medical expert expertise. The primary goal of medical image segmentation and classification is not only to achieve high precision, but also to classify which form of virus infects the patient.  Here we are going to perform segmentation of blood cells then classify different categories of Hepatitis virus that affect human blood using the efficient algorithm taken from above comparative analysis performed in previous work.