Myelodysplastic syndrome risk assessment using priority linked correlated feature set using ResNet50

Abstract

Myelodysplastic syndrome (MDS) is more common as people get older, and the best way to treat these conditions is to catch them early. Acute myeloid leukemia (AML) is a more aggressive form of MDS; about 30 % of cases advance to this disease. The key to intervening before MDS develops into AML is early identification. If doctors can diagnose MDS early, they can start treatment sooner, which could slow the disease’s progression and enhance patients’ quality of life. But because it is so similar to other conditions, MDS is frequently misdiagnosed. Early detection of MDS can let medical experts to provide better treatment, which may slow the disease’s progression and boost survival rates. Assessment of Bone Marrow (BM) histopathology is used to diagnose MDS and myeloproliferative neoplasm (MPN) by identifying dysplastic cellular shape, cellularity, and blast excess. Deep Learning (DL) algorithms can account for the genome’s numerous complicated interconnections, allowing for the creation of more accurate prognostication models. The ResNet50 network consists of 50 layers: 48 convolutional layers, 1 MaxPool layer, and 1 average pool layer. This research presents a Priority Linked Correlated Feature Set using ResNet50 (PLCFS-ResNet50) for risk assessment of MDS. The proposed model uses the feature subset for training the Resnet50 model and for risk assessment so that proper diagnosis can be done in advance. The proposed model achieves 98.6 % accuracy in feature set generation and 98.7 % accuracy in risk assessment of MDS. The proposed model when contrasted with the traditional model exhibits better prediction levels.