MojoVision: An Extended Conceptual Framework for Psoriasis Detection and Enhanced Treatment Suggestion

Psoriasis is a chronic immuno-dermatological condition affecting individuals of all ages and significantly impacting their quality of life. The proposed system enhances psoriasis management by integrating a hybrid deep learning technique into a clinical decision support framework, an advancement from a previously developed Computer-Aided Diagnosis (CAD) tool called MojoVision. This approach involves analyzing dermatological images to detect the presence of psoriasis and then categorizing it into one of seven distinct variants. Central to this system is the treatment suggestion to advise on the most effective treatment options based on the specific variant and severity of symptoms observed in patients. In this research, the enhancement to the decision support tool includes integrating Natural Language Processing (NLP) with MojoVision to create a multi-modal decision support tool. By incorporating NLP, the system gains a holistic understanding of each case, considering visual dermatological data and textual medical records, thus enabling more precise diagnoses and personalized treatment recommendations. This conceptual framework demonstrates high practicality in diagnostic and treatment advisory capacities, offering a significant advancement in the clinical management of psoriasis.