Deep Learning Algorithms for Classification and Prediction of Acute Lymphoblastic Leukemia

The death due to cancer increases daily due to late or even no diagnosis. It is necessary for early detection and treatment of such fatal cancers, and avoiding carcinogenic agents is an important step for the prevention of cancers. The majority of the cancers are detected by computed tomography (CT), magnetic resonance imaging (MRI), and even X-ray images. There are different techniques used by the doctors, like pattern recognition; seven-point detection; and Asymmetry, Border, Color, and Diameter (ABCD) method. However, these methods may not be efficient always. Hence, currently, there is a huge scope for automation in the medical field for better performance and early diagnosis of fatal diseases to prevent further complexities and also for the betterment of mankind. There are several automation techniques—among which, deep learning techniques are considered much efficient for the diagnosis of diseases. The automation of the classification of acute lymphoblastic leukemia is demonstrated by using Kaggle datasets in this chapter. To achieve this purpose, the variants of CNN along with different activation functions are experimented and achieved the highest accuracy of 81.04% with ResNet50, which are better in diagnosis compared to the efficiency of manual diagnosis, reducing the risk of cancer in mankind.