Elephant herding optimized features-based fast RCNN for classifying leukemia stages.

Abstract

Background: Leukemia is a cancer that develops in the bone marrow and blood that is brought on by an excessive generation of abnormal white blood cells. This disease damages deoxyribonucleic acid (DNA), which is associated with immature cells, particularly white blood cells. It is time-consuming and requires enhanced accuracy for radiologists to diagnose acute leukemia cells.

Objective: To overcome this issue, we have studied the use of a novel proposed LEU-EHO NET.

Methods: LEU-EHO NET has been proposed for classifying blood smear images based on leukemia-free and leukemia-infected images. Initially, the input blood smear images are pre-processed using two techniques: normalization and cropping black edges in images. The pre-processed images are then subjected to MobileNet for feature extraction. After that, Elephant Herding Optimization (EHO) is used to select the relevant feature from the retrieved characteristics. Finally, Faster RCNN is trained with the selected features to perform the classification task and discriminate between Normal and Abnormal.

Results: The total accuracy of the proposed LEU-EHO NET is 99.30%. The proposed LEU-EHO NET model enhances the overall accuracy by 0.69%, 16.21%, 1.10%, 1.71%, and 1.38% better than Inception v3 XGBoost, VGGNet, DNN, SVM and MobilenetV2 respectively.

Conclusion: The approach needs to be improved so that overlapped cells can be segmented more accurately. Additionally, future work might improve classification accuracy by utilizing different deep learning models.