Blood Cancer Diagnosis using Deep Learning: Enhancing Accuracy in Leukemia Detection

Abstract

Blood problems are frequently discovered by visual inspection under a microscope. It might help classify a number of blood-related disorders in order to facilitate the identification of different leukemia conditions. Leukemia is the most common cancer that affects the body's red blood cells. Even in the absence of any outward signs, it can progressively damage all of the body's internal organs, leading to a host of other illnesses. Early blood disease identification is difficult since current technologies take longer. The creation of a technique that might aid in the classification of leukemia prediction is described in the article. The accurate identification of blood cancer is the primary objective of this study. Certain leukemia diseases, such acute myeloid leukemia (AML) and lymphoblastic leukemia (ALL), prevent cells from growing and protecting every component of the blood, which can cause cancer and other illnesses that can harm the blood in various ways. This study examined three distinct blood cell classifications: neutrophils, a subset of normal blood cells, and two distinct kinds of cancer cells. Clinical methods are not particularly good in predicting leukemia because symptoms include fatigue, sickness, fatigue, and loss of appetite. In order to obtain the best results for identifying blood cancer utilizing the most precise categories possible using a deep learning approach, the strategy was designed to predict blood cancer using DL techniques for picture automated identification and splitting methodology. used 6000 photos in this study. In the process, we showcased and compared several deep learning models, such as Inception V3, Mobile Net V2, InceptionResnetv2, and VGG16 & VGG19. The assessments that were done on the all five models showed that get the best accuracy of 100% on the dataset, which is excellent. Following accuracy in the detection of a certain kind of blood cancer.