Novel approach to integrate various feature extraction techniques for the Visual Question Answering System with skeletal images in the healthcare sector

Abstract

In the realm of medical science, one of the most challenging concepts to grasp is the Medical Imaging Query Response System. The comprehension and classification of the diverse representations of the human body require a significant degree of effort and expertise. Furthermore, it is imperative for users within the healthcare sector to rigorously validate the system. In the domain of human health, a plethora of imaging techniques, including MRI, CT, ultrasound, X-ray, PET-CT, and others, play a pivotal role in the identification of medical issues. These technologies are instrumental in supporting both patient engagement and clinical decision-making. However, the utilization of models, techniques, and datasets for processing textual and visual information introduces complexities that can at times impede the provision of pertinent clinical solutions. The overarching objective of the proposed approach is to conduct a comprehensive comparative analysis of various feature extraction methodologies for both visual and textual information within the Visual Question Answering (VQA) system, focusing on human skeletal images. This endeavor is aimed at enhancing the VQA system's performance with newer datasets and addressing any limitations inherent in existing models. In addition, this research initiative seeks to enable researchers to identify and optimize novel methods that enhance the accuracy of the VQA system. The models under scrutiny in this analysis encompass various methods of feature extraction that help to improve the model and quality of the healthcare industry. The researcher will find the proper methodology for different datasets. To gauge the efficacy of each model in delivering the desired outcomes, an array of metrics will be employed, including classification measurement accuracy, F-classification, C-true positive rate (CTPR), C-precision, C-recall, C-sensitivity, and C-false negative rate (FNR). These metrics are designed to enhance the accuracy of any dataset and optimize the performance of both visual and textual components to ensure accurate responses to the posed queries.