Automatic and Robust Technique for Segmentation and Classification of Acute Lymphoblastic Leukemia using Adaptive Multi-Dilated Residual Attention Network and Heuristic Strategy

Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/3673 2018 2018 Automatic and Robust Technique for Segmentation and Classification of Acute Lymphoblastic Leukemia using Adaptive Multi-Dilated Residual Attention Network and Heuristic Strategy Department of Information Technology, Dr.M.G.R. Educational and Research Institute Abirami Abirami Department of Computer Science and Engineering, Dr.M.G.R. Educational and Research Institute Victo Sudha George. G. Department of Information Technology, Dr.M.G.R. Educational and Research Institute Dahlia Sam. S. Leukemia is a very dangerous kind of malignancy troubling the blood or bone marrow in all age categories, both in adults and children. The deadly and threatening kind of leukemia is named Acute Lymphoblastic Leukemia (ALL). The accurate and automated ALL diagnosis of blood cancer is complex work. Medical experts and hematologists in the bone marrow and blood samples detect it by employing a high-quality microscope. The manual classification is observed as tiresome and is restricted by varying expert considerations and other attributes. Presently, the Convolutional Neural Networks (CNNs) have become an acceptable mechanism for analyzing the medical image. However, for attaining outstanding performance, conventional CNNs normally demand large data sources for better training. Thus, to alleviate the existing complexities, we implemented an effective ALL detection system using deep learning. At first, the necessitated images are aggregated from global resources of data. Further, the garnered images are inputted into the Optimized Trans-Res-Unet+ (OTRUnet+)-based segmentation model. Here, the Fitness-aided Position Updating in the Social engineering Algorithm (FPUSA) for improving the segmentation process’s efficacy optimally tunes the OTRUnet+ technique parameters. In addition, the segmented images are taken to perform the classification process using the Adaptive Multi-Dilated Residual Attention Network (AMDRAN); here several parameters are optimally tuned by the same FPUSA to enrich the classification process. Finally, the suggested AMDRAN technique offered the ALL classified output. The effectiveness of the designed ALL detection system is explored with several existing systems to display its enhanced performance over other models 2025 2025 224 252 10.54216/FPA.190217 https://www.americaspg.com/articleinfo/3/show/3673