MorphoVisionMapper: A Predictive Model for Structural and Morphological Image Analysis in Biomedical Applications

The survival rates of people with transfusion-dependent thalassemia major (TM) have been substantially raised by transfusion treatment. Still, the later iron accumulation compromises organs like the endocrine systems, liver, and heart. The survival rates of these patients have therefore greatly risen. Still, the main cause of death in the United States is heart-related problems. Myocardial iron deposition can develop without the simultaneous involvement of other solid organs. Conversely, in other tissues, the heart may stay unaltered even in cases of extreme siderosis. Although iron chelation therapy reduces the incidence of hemosiderosis, this treatment has side effects and might not be accessible to every patient because of financial constraints. Improvement of customized patient treatment depends on close observation of the iron content and functioning of every organ. This is crucial for improving patient treatment. The results of this work indicate that structural and morphological analysis should be used in the evaluation of myocardial iron overload (MIO). In this design, the prediction model is constructed using Gentle Adaptive Boosting Ensemble Learning (GABEL). The performance is evaluated using the MATLAB simulation and obtained the prediction accuracy of 99.17% using our proposed method.