Automated classification of acute leukemia on a heterogeneous dataset using machine learning and deep learning techniques

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 stateof-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 VGG16 are fine tuned and 98% for DenseNet121 along with support vector machine. For three-class classification task, an accuracy of 95% is obtained for ResNet50 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.