Marine Predators Algorithm with Deep Learning-Based Leukemia Cancer Classification on Medical Images

Abstract

In blood or bone marrow, leukemia is a form of cancer. A person with leukemia has an expansion of white blood cells (WBCs). It primarily affects children and rarely affects adults. Treatment depends on the type of leukemia and the extent to which cancer has established throughout the body. Identifying leukemia in the initial stage is vital to providing timely patient care. Medical image-analysis-related approaches grant safer, quicker, and less costly solutions while ignoring the difficulties of these invasive processes. It can be simple to generalize Computer vision (CV)-based and image-processing techniques and eradicate human error. Many researchers have implemented computer-aided diagnostic methods and machine learning (ML) for laboratory image analysis, hopefully overcoming the limitations of late leukemia detection and determining its subgroups. This study establishes a Marine Predators Algorithm with Deep Learning Leukemia Cancer Classification (MPADL-LCC) algorithm on Medical Images. The projected MPADL-LCC system uses a bilateral filtering (BF) technique to pre-process medical images. The MPADL-LCC system uses Faster SqueezeNet with Marine Predators Algorithm (MPA) as a hyperparameter optimizer for feature extraction. Lastly, the denoising autoencoder (DAE) methodology can be executed to accurately detect and classify leukemia cancer. The hyperparameter tuning process using MPA helps enhance leukemia cancer classification performance. Simulation results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches.