A Diagnostic Model to Support Follicular Lymphoma Classification of Hematoxylin and Eosin Stained Images

Abstract

Recent improvements in histopathology images have sped up the assessment of perceptual issues while predicting problems related to reader subjectivity. This variance causes different prognosis reports and alters the course of treatment. A computer-assisted grading system based on machine learning and employing medical images is required to prevent under- and over-treating patients. newlineSeveral challenges must be identified and overcome before developing procedures for computer-aided interpretation of high-resolution histopathology images using image analysis. The main goal is to create algorithms to perform essential image analysis tasks. These tasks include precise and flexible segmentation of cytological components to enable further processing, generation of biologically relevant and computationally feasible features, and their mathematical representations to distinguish between various tissue types. Additionally, the algorithms should be able to detect tissue structures that have prognostic significance and align tissue sections prepared with different stains to incorporate spatial information. The efficacy of the recommended protocols is evidenced by the utilization of HandE-stained images of follicular lymphoma (FL). The evaluation and utilization of strategies for classifying FL tissue samples that have undergone HandE staining are conducted. newline newline