Deep Learning For Leukemia Diagnosis

Abstract

One major worldwide health concern is leukemia, a form of blood cancer. A better prognosis for the patient and successful therapy depend on an early and accurate diagnosis. Traditional diagnostic techniques have typically depended on the time-consuming and prone to mistake process of manual microscopic examination of blood smears. To address these issues, we present a novel deep learning-based automatic leukemia diagnosis system. Our approach extracts high-resolution information from microscopic images of blood cells using a Convolutional Neural Network (CNN). The CNN can reliably distinguish between leukemic and healthy cells because it has been trained on a large number of tagged pictures. The experimental results demonstrate the model's excellent performance and suggest that it has the potential to revolutionize leukemia diagnosis by facilitating quicker and more accurate detection.