A ROBUST ENSEMBLE SEGMENTATION APPROACH AND OPPOSITION-BASED RAIN OPTIMIZATION ALGORITHM FOR ENHANCING ACUTE LYMPHOBLASTIC LEUKEMIA (ALL) DETECTION

Abstract

Generally, leukemia is one of the blood cancers that can lead to death. A huge amount of immature WBCs in the bone marrow affect the healthy cells and this has become the leading cause of leukemia disease. Moreover, “Acute Lymphoblastic Leukemia (ALL)” is a type of blood cancer that is generally categorized with a huge amount of immature lymphocytes that are given as blast cells. But, the analysis of this model is dependent, boring as well as time-consuming based on the skills of hematologists. So, there is a requirement for the most desirable techniques to tackle these restrictions. Additionally, the accurate and automated diagnosis of ALL is a crucial task. In this case, an automated detection model of ALL using ensemble segmentation and a heuristic-assisted layer-improved hybrid deep learning approach is implemented. In the initial stage, the raw images are collected from benchmark datasets. Further, the image pre-processing is undergone by contrast enhancement and filtering process. After pre-processing, the image segmentation is carried out by ensemble segmentation, which is acquired by the region growing, K-medoids, and Fuzzy C-Means (FCM). From the segmented images, based on the mutual information, the pixel is selected optimally, in which the optimal pixel is identified by Opposition-based Rain Optimization Algorithm (OROA). Subsequently, the optimal pixels are fed as input to the Layer Improved Hybrid DenseNet and ResNet (LIH-DRNet) model that is constructed with DenseNet and ResNet, where some hyperparameters are tuned optimally by the improved ROA. At last, the performance is evaluated with diverse performance metrics. Thus, the findings reveal that the developed hybrid deep learning method achieves a higher detection rate to ensure the effectiveness of the model.