LUK-SEGNET: Classification of Leukemia Using an Elephant Herding Optimization-based Duo SegNet

Leukemia is a bone marrow-derived malignancy that is distinguished by aberrant white blood cell proliferation. Leukemic cells proliferate rapidly in the blood and travel to the brain, neurological system, spleen, liver, and lymph nodes, among other areas of the body. In the early stages of the disease, leukemia is difficult to diagnose because current technologies are less time-efficient. To overcome this, a novel LUK-SEGNET has been proposed for leukemia classification using blood smear images from the ALL-IDB dataset. The input images are pre-processed by resizing and cropping black edges from the blood smear images. The Duo-SegNet feature extraction technique extracts the high-dimensional features from the images. The proposed method uses an elephant herding optimization algorithm to select the relevant features from the images. The performance of the proposed method is evaluated using accuracy, precision, recall, specificity and f1-score. The proposed LUK-SEGNET method achieves an accuracy rate of 99.74% in the normal class and 99.87% in the abnormal classes. The proposed LUK-SEGNET method achieves an overall accuracy of 0.05%, 0.19%, and 0.11% better than DL4ALL, ResNet-152 and Inception V3, respectively.