A Modified Conventional Neural Network for Detecting and Classifying Leukemia
Utkarsh Nautiyal, Ashish Bhatt, Rahul Singh Chauhan, Ruchira Rawat, Rupesh Gupta · 2024
A dangerous blood cancer mostly affecting white blood cells is called leukemia. It is difficult to detect and classify early. In particular, leukemia forms such as acute myeloid leukemia (AML), acute lymphoblastic leukemia (ALL), chronic myeloid leukemia (CML), and chronic lymphoblastic leukemia (CLL) may be reliably identified and classed using standard neural networks. The need of promptly identifying and differentiating between leukemia subtypes for treatment planning and prognostication is emphasized in the study. In order to uncover distinctive traits, patterns, and markers connected to various forms of leukemia, this work look into the use of conventional neural networks on a variety of datasets. By using machine learning approaches to boost the precision and effectiveness of early-stage diagnosis, this research aims to enhance patient outcomes, reduce healthcare costs, and advance medical diagnostics.The findings show that traditional neural networks have immense promise for improving patient outcomes, identifying illnesses, and strengthening healthcare systems. By utilizing state-of-the-art computational methods, our study contributes to the continuous efforts to enhance leukemia diagnosis.