ALL-018 Application of Machine Learning in the Diagnosis of Acute Lymphoblastic Leukemia

Abstract

The ordinary morphologic diagnosis of acute lymphoblastic leukemia (ALL) by a pathologist depends on examining the patient's peripheral blood (PB) together with the bone marrow (BM) blood films. However, this manual aspect of diagnosis is susceptible to discrepancies. We now have a newly introduced technology that allows us to overcome individual variation in the diagnosis of ALL, so-named machine learning, which depends on a complex, preprogrammed convolutional networks matrix. Challenging machine-aided systems that utilize microscopic blood film images to recognize and diagnose ALL based on a preprogrammed deep convolutional neural network (CNN), i.e., machine learning algorithms. We collected a dataset of images composed of PB and BM smear images of two classes: ALL and normal control blood. We analyzed 192 samples of digital images: 96 images of patients with ALL and 96 images of healthy normal controls. For each smear sample, we collected the results of clinical data (clinical history and examination) and laboratory data (morphological, cytochemical, and immunophenotyping assessment). We challenged seven types of CNN models to diagnose ALL: AlexNet, VGG16, VGG19, GoogLeNet, ResNet50, ResNet101, and Inception-v3. Comparing the ability of seven models to diagnose ALL revealed that the AlexNet had the lowest accuracy of 95.51%, followed by VGG16 (92.13%) and VGG19 (93.83%). Inception-v3 had the highest accuracy (99.98%) and was able to detect almost all ALL cases. The statistical measures of the Inception-v3 performance revealed promising results in detecting ALL cases: the sensitivity, specificity, and accuracy of Inception-v3 reached 99.98% for detection of ALL.