Autonomous Leukemia Detection Scheme Based on Hybrid Convolutional Neural Network Model Using Learning Algorithm

In the human body, one of the fatal diseases of white blood cells is leukemia that affects the bone marrow as well as blood. In this work, the leukemia image is pre-processed with a data augmentation model in which the noise and over-fitting are eliminated and the data augmentation increases the size of the datasets. We used Hybrid Convolutional Neural Network with Interactive Autodidactic School (HCNN-IAS) algorithm, which performs feature extraction, fusing, and classification operations. At first, the local and global features are extracted from the leukemia image. After that, the self-attention in CNN combines both local and global features. Finally, the HCNN-IAS algorithm effectively categorizes different classes of leukemia such as healthy, Acute Lymphocytic Leukemia (ALL), Acute Myeloid Leukemia (AML), Chronic myeloid leukemia (CML), and Chronic Lymphocytic Leukemia(CLL). The proposed model is implemented in an Internet of Medical Things (IoMT) platform to offer the appropriate treatment to the patients within the comfort of their homes. The images of the dataset were acquired from the ASH image bank. The experimental result demonstrates better and higher classification accuracy in terms of leukemia detection with reasonable accuracy, precision and recall rates of about 99%.