DEEP EVOLUTIONARY LEARNING MODEL FOR PRECISION MUTATION ANALYSIS IN MYELODYSPLASTIC AND LEUKEMIC CELLS

Myelodysplastic Syndromes (MDS) are inherited bone marrow stem cell disorders. Comparing the genetic makeup of healthy and MDS patients may help identify and treat the disease. MDS gene expression data is being used by biological scientists to study coding and non-coding gene activities. Categorization and risk stratification are necessary to make clinical decisions concerning Acute Myeloid Leukemia (AML) patients. This study employs two processed gene expression datasets to identify the most critical genes for effective clustering. This research incorporates a deep learning strategy for categorization from segmented images based on the Visual Geometric Group (VGG16) Convolution Neural Network (CNN) architecture. The solution to this problem is a VGG16 deep learning architecture that uses Particle Swarm Optimization (PSO) for disease classification and accurate analysis of gene mutations. Using the PSO technique to fine-tune VGG16's hyperparameters improves feature extraction efficiency and classification performance. A publicly available dataset of pre-processed bone marrow smear images is used to validate the system. This research proposes a Radiation Associated and Therapy Related Rearrangements and Mutations of Gene Analysis for Myelodysplastic Syndrome and Acute Myeloid Leukemia Classification using PSO optimized VGG16 (RATR-RMGA-MDS-AML-PSO-VGG16) for accurate classification levels. A 99.2% classification accuracy, 99.4% VGG16 feature processing accuracy and 99.4% Mutations of Gene Analysis Accuracy Levels were achieved by the suggested PSO-VGG16 model in the experiments, which is a significant improvement over baseline VGG16 and other traditional machine learning approaches. These results show that PSO-VGG16 integration is a powerful and accurate diagnostic tool for MDS and AML classification, which opens new possibilities for treatment plans.