Skin disease diagnosis using image analysis and natural language processing

TL;DR

This paper explores using deep learning for skin disease diagnosis through image analysis and natural language processing to improve healthcare accessibility in Zambia, addressing staff shortages and reducing patient travel.

Contribution

It introduces a deep learning model for clinical diagnosis using image analysis, aiming to automate tasks and ease medical staff workload in resource-limited settings.

Findings

Potential to automate skin disease diagnosis

Improves healthcare accessibility in Zambia

Reduces workload on medical practitioners

Abstract

In Zambia, there is a serious shortage of medical staff where each practitioner attends to about 17000 patients in a given district while still, other patients travel over 10 km to access the basic medical services. In this research, we implement a deep learning model that can perform the clinical diagnosis process. The study will prove whether image analysis is capable of performing clinical diagnosis. It will also enable us to understand if we can use image analysis to lessen the workload on medical practitioners by delegating some tasks to an AI. The success of this study has the potential to increase the accessibility of medical services to Zambians, which is one of the national goals of Vision 2030.