Automated classification of acute leukemia on a heterogeneous dataset using machine learning and deep learning techniques. (February 2022)

• Record Type: Journal Article
• Authors: Abhishek, Arjun; Jha, Rajib Kumar; Sinha, Ruchi; Jha, Kamlesh
• Is Part Of: Biomedical signal processing and control. Volume 72(2022)Part B
• Publication Date: 2022-02
• Subjects: Acute myeloid leukemia -- Acute lymphoblastic leukemia -- Heterogeneous dataset -- Machine learning -- Deep learning; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.103341
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: A dataset consisting of microscopic images of Acute Myeloid Leukemia, Acute Lymphoblastic Leukemia and normal cases is proposed. A heterogeneous/mixed dataset is formed by adding images of a publicly available dataset, i.e., ALL-IDB to the proposed dataset. Proposed and mixed datasets are used to detect leukemia with the help of machine learning and deep learning techniques. Obtained results are analysed quantitatively and qualitatively. Abstract: Today, artificial intelligence and deep learning techniques constitute a prominent part in the area of medical sciences. These techniques help doctors detect diseases early and reduce their burden as well as chances of errors. However, experiments based on deep learning techniques require large and well-annotated dataset. This paper introduces a novel dataset of 500 peripheral blood smear images, containing normal, Acute Myeloid Leukemia and Acute Lymphoblastic Leukemia images. The dataset comprises almost 1700 cancerous blood cells. The size of the dataset is increased by adding images of a publicly available dataset and forming a heterogeneous dataset. The heterogeneous dataset is used for the automated binary classification task, which is one of the major tasks of the proposed work. The proposed work perform binary as well as three-class classification tasks involving state-of-the-art techniques based on machine learning and deep learning. For binary classification, the proposed work achieved an accuracy of 97 % when fully connected layers along with the last three convolutional layers of VGG 16 are fine tuned and 98 % for DenseNet 121 along with support vector machine. For three-class classification task, an accuracy of 95 % is obtained for ResNet 50 along with support vector machine. The preparation of the novel dataset is done under the opinion of various expertise that will help the scientific community for medical research supported by machine learning models.