Detection of chronic lymphocytic leukemia using Deep Neural Eagle Perch Fuzzy Segmentation – A novel comparative approach

Abstract

With a high mortality rate worldwide, Chronic Lymphocytic Leukemia (CLL) poses a serious health risk. Radiologists view the ability to detect blood tumor cells as both important and difficult. It is challenging for many of the current studies to properly examine these blood cancer cells. An intellectual system for separating healthy cells from tumor cells is suggested in this research work. This research primarily depends on the effective implementation of computed tomography (CT) samples of both benign and malignant blood samples. In this research paper, novel methodologies called public active contour pixel mapping and Deep Neural Eagle Perch Fuzzy Segmentation (DNEPFS) are proposed, which help in detecting the blood cell boundary of the unaffected cells. It is quite similar to an eagle using the claw searching algorithm. Here, the eagle uses its sharp claws to tear the prey’s skin during the search for its food. 99.64% and 99.32% accuracy are obtained during the research work for benign and malignant nodules using MATLAB R2023a. The computational complexity was found to be less, which is 7.13 s. Different segmentation techniques are used for comparative analyses. According to the research results, this proposed flow is superior to all segmentation techniques currently in use for the accurate detection of tumor cells. Based on the expert's annotation on online samples and clinical samples, the methodology has been validated to prove their effectiveness and throw light on the life of affected patients to resume normalcy – live long. The research work was tested in real-time clinical samples which deliver promising and encouraging results in leukemia detection procedures.