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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. [ABSTRACT FROM AUTHOR]

Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization problems. This approach aims to leverage the strengths of multiple algorithms, enhancing solution quality, convergence speed, and robustness, thereby offering a more versatile and efficient means of solving intricate real-world optimization tasks. In this paper, we introduce a hybrid algorithm that amalgamates three distinct metaheuristics: the Beluga Whale Optimization (BWO), the Honey Badger Algorithm (HBA), and the Jellyfish Search (JS) optimizer. The proposed hybrid algorithm will be referred to as BHJO. Through this fusion, the BHJO algorithm aims to leverage the strengths of each optimizer. Before this hybridization, we thoroughly examined the exploration and exploitation capabilities of the BWO, HBA, and JS metaheuristics, as well as their ability to strike a balance between exploration and exploitation. This meticulous analysis allowed us to identify the pros and cons of each algorithm, enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization performance. In addition, the BHJO algorithm incorporates Opposition-Based Learning (OBL) to harness the advantages offered by this technique, leveraging its diverse exploration, accelerated convergence, and improved solution quality to enhance the overall performance and effectiveness of the hybrid algorithm. Moreover, the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems, providing a comprehensive assessment of its efficacy and applicability in diverse problem domains. Similarly, the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms, where mean and standard deviation values were utilized as evaluation metrics. This rigorous comparison aimed to assess the performance of the BHJO algorithm about its counterparts, shedding light on its effectiveness and reliability in solving optimization problems. Finally, the obtained numerical statistics underwent rigorous analysis using the Friedman post hoc Dunn's test. The resulting numerical values revealed the BHJO algorithm's competitiveness in tackling intricate optimization problems, affirming its capability to deliver favorable outcomes in challenging scenarios. [ABSTRACT FROM AUTHOR]