Digital Image Processing and Python for Acute Lymphoblastic Leukemia (ALL) Detection using Convolution Neutral Network (CNN) Algorithm

Dr. M. Sangeetha, S. D. Akila

Abstract: The timely identification and diagnosis of leukemia, or the precise differentiation of malignant leukocytes at the lowest possible cost in the early stages of the disease, is a significant problem in the field of illness diagnostics. Despite the high prevalence of leukemia, laboratory diagnosis institutes have limited flow cytometer equipment and labor-intensive procedures. The goal of the current systematic review was to examine the literature on machine learning-based leukemia identification and classification. The promise of machine learning (ML) in the diagnosis of diseases served as the driving force behind it. Leukemia is a malignancy of the blood-forming tissues that affects the lymphatic and bone marrow systems. If caught early, treatment options are more effective. This study created a novel classification model for blood microscopic images that can identify between images with and without leukemia.

Keywords: leukemia diagnosis, machine learning, blood microscopic images, systematic review, cost-effective