Harnessing the Power of Multimodal Data: Medical Fusion and Classification

Abstract

In the field of medical diagnosis, combining different types of information like text, images, and audio is a big step forward in making patient assessments more accurate. This research introduces an innovative method to bring together and categorize these different types of data. This method fills an important gap in current research [50, 54]. Proposed approach focuses on turning each type of data—text, images, and audio—into useful numbers. Text data is processed to extract meaning and context, while images are analysed using advanced computer techniques to capture important visual details. We also carefully examine audio data to extract important sound features, which is often overlooked but can be a valuable source of diagnostic information [48]. What makes our method special is how we combine these different types of data. We designed a strategy to blend these diverse sets of numbers into a single, enriched representation. This approach keeps the unique characteristics of each data type intact while harnessing their combined power for diagnosis [22, 29]. After combining the data, we use a well-chosen classification model that's known for its ability to make sense of complex data, especially in medical diagnosis scenarios [67, 71]. Proposed approach is rigorously assessing our method using a set of strong metrics that measure not only how accurate it is but also how reliable and valid it is for diagnosis [90, 94]. The results of this study mark a significant step forward in the field of combining different types of data, showing how it can greatly improve medical diagnosis. This method has the potential to revolutionize healthcare, enabling more precise and comprehensive data-driven decisions [143, 156].