Abstract
Leukemia is a cancer of blood cells in the bone marrow that affects both children and adolescents. The rapid growth of unusual lymphocyte cells leads to bone marrow failure, which may slow down the production of new blood cells, and hence increases patient morbidity and mortality. Age is a crucial clinical factor in leukemia diagnosis, since if leukemia is diagnosed in the early stages, it is highly curable. Incidence is increasing globally, as around 412,000 people worldwide are likely to be diagnosed with some type of leukemia, of which acute lymphoblastic leukemia accounts for approximately 12% of all leukemia cases worldwide. Thus, the reliable and accurate detection of normal and malignant cells is of major interest. Automatic detection with computer-aided diagnosis (CAD) models can assist medics, and can be beneficial for the early detection of leukemia. In this paper, a single center study, we aimed to build an aggregated deep learning model for Leukemic B-lymphoblast classification. To make a reliable and accurate deep learner, data augmentation techniques were applied to tackle the limited dataset size, and a transfer learning strategy was employed to accelerate the learning process, and further improve the performance of the proposed network. The results show that our proposed approach was able to fuse features extracted from the best deep learning models, and outperformed individual networks with a test accuracy of 96.58% in Leukemic B-lymphoblast diagnosis.
Highlights
The implementation of our proposed model for acute lymphoblastic leukemia (ALL) classification consists of the following steps: transfer learning using popular pre-trained DCNN architectures, hyper-parameter tuning of learning parameters, and ensemble technique in order to extract more discriminative features from a dataset
This study was mainly based on a dataset of classification of normal versus malignant cells in B-ALL white blood cancer microscopic images (ISBI 2019) provided by SBI-Lab [11,12,43,44], which is available to the public at [21]
We presented an aggregation-based deep learning method for ALL classification, with an automation system to discern between healthy and cancer cases that strengthens the decision taken by the physician and reduces the workload
Summary
Leukemia is a cancer of blood cells in the bone marrow that affects both children and adolescents. Acute leukemia can be subdivided into acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML), based on the type of blood cell that is affected [1]. In ALL, lymphocytes, a type of white blood cell (WBC), in the bone marrow do not mature properly into normal cells and reproduce out of control. According to the data provided by [3], In 2019, about 6150 new cases of Leukemia of type be taken into account in ALL diagnosis. According to the data provided by [3], In 2019, about 6150 new cases of Leukemia of type ALL were diagnosed, and about 1520 patients died of ALL, including both children and adults in the United AStLaLtesw. Tdheme oAnLsLtralytemdpihnobFliagsutsreva1ry(bionttsoimze,raonwd).thUesisnhgapCeAoDf nsuycslteeimiss vwerityhirtrheeguinlater,garastidoenmoofnsatdrvataendceidn Fmigeudricea1l (ibmoattgoemprroowce).ssUinsginganCdAmDascyhsitneme slewarinthinthgeminettehgordatsi,own oefcaadnvapnrcoevdidmeerdeiacla-ltiimmeagaenpalryosciesssainndg abnetdtemr adcehciinsieonle-amrnaiknigngmteotohlos.ds, we can provide real-time analysis and better decision-making tools
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