Design and development of a convolutional neural network based on human cognitive attention mechanism for automatic classification of leukemia.

Cancer occurs when healthy cells in the body grow abnormally and out of control. Leukemia is a type of cancer that affects White Blood Cells (WBCs) and can cause a lethal infection and early death. Identification and classification of different types of leukemia are performed manually and automatically. The doctors analyze blood samples under a microscope and consider any changes in the number and structure of WBCs as a sign of cancer in the manual method. It is a time-consuming, inaccuracy-prone process that depends on the expertise and skill of the physician and the type of laboratory equipment. In recent years, more automated methods of identifying and classifying leukemia have been developed with the help of Artificial Intelligence (AI) and Computer Vision (CV), with the aim of overcoming the challenges of manual approaches. This paper introduces two types of attention blocks, Parallel Cognitive Attention Block (PCAB) and Sequential Cognitive Attention Block (SCAB), to integrate into the architecture of any Convolutional Neural Network (CNN). Each of the proposed attention blocks is composed of the channel and spatial attention sub-blocks. They extract the structure and location of WBCs in the feature maps, similar to the ventral and dorsal streams in the human brain. The PCAB and SCAB were embedded in the architecture of the ResNet18 and MobileNetv4. The baseline and attention-based networks are trained, validated, and tested by two types of data splitting on the four leukemia datasets, including ALL, ALL-IDB2, C-NMC, and Mixture-Leukemi (ALL-IDB2+Munich AML Morphology), with the same experimental conditions for 30 epochs. The classification results demonstrate that the proposed model (MobileNetv4PCAB) achieved better performance metrics than others on all datasets in the test steps. It showed that the suggested model achieved the accuracy values of 100%, 100%, 93.61%, and 99.4%, and the F1-score values of 100%, 100%, 95.64%, and 99.3% with ALL, ALL-IDB2, C-NMC, and Mixture-Leukemia datasets, respectively. We confirmed that the proposed model outperforms existing state-of-the-art methods.