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

How to Cite?: Dr. M. Sangeetha, S. D. Akila, "Digital Image Processing and Python for Acute Lymphoblastic Leukemia (ALL) Detection using Convolution Neutral Network (CNN) Algorithm", Volume 13 Issue 4, April 2024, International Journal of Science and Research (IJSR), Pages: 858-861, https://www.ijsr.net/getabstract.php?paperid=SR24410105704, DOI: https://dx.doi.org/10.21275/SR24410105704