Image Classification for Optimized Prediction of Leukemia Cancer Cells using Machine Learning and Deep Learning Techniques

Leukemia is a type of cancer that has a high mortality rate that affects people of all ages but is most common in children aged 5–8 years. It is usually diagnosed using one of two methods. First method is the traditional way of manually analyzing the blood samples retrieved from the microscopic images, however there is a disadvantage in this method, when the leukemia cells and normal cells are similar to each other the diagnosis process becomes more complex. The second method uses the image classification algorithms such as Deep Learning (DL) algorithms, Convolution Neural Networks (CNNs). There is an emerging trend in Machine Learning (ML) and Deep Learning (DL) models to perform image classification. This study intends to analyze ML & DL techniques for the detection and prediction of leukemia. In addition to the existing literature, this study aims to identify different ML and DL based image classification algorithms that leverage best prediction accuracy for leukemia cancer cells.