Dhanvantari: An intelligent diagnosis tool to classify malignant skin disease and Lung conditions using Deep Learning

ABSTRACT

The pandemic due to COVID-19 has created a huge gap in the medical field leading to a reduction in the efficacy of this field. To improve this situation, we propose a solution 'Dhanvantari'. A medical app that is powered by Artificial Intelligence performs a task where the diagnosis is done by computer vision observing CT scans, MRIs, and also some skin diseases. Dhanvantari focuses mainly on the combination of CT scans and skin disease classifications. In this paper, a novel approach has been proposed for developing a supervised model for the classification of skin disease and lung ailments (that is to identify a healthy lung with an infected lung due to pneumonia) through analog to digital image processing. This app helps the user in analyzing conditions and if any abnormalities are detected then alerts the user about it. This is a primary service care application developed to reduce the number of false cases hence only alerting the user if a complication is observed. The proposed approach utilizes a camera and computational device or mobile. Two datasets from Kaggle that had 9 classes of malignant skin disease and 2 lung conditions were used to train the model. Design, training, and the testing of the algorithm were performed with the help of colab. Generally, a standard test for malignant skin disease requires sample gathering and conduction of various tests. All these consume a lot of time. The other method is laser or radiation-induced procedures that might be harmful and lead to exposure of unwanted radiation to patients. The proposed 'Dhanvantari' requires the patient/user to use a camera to take a picture of the affected area (in case of skin condition) and it provides the primary diagnosis. This approach aids the doctors in quick decision-making during diagnosis and reduce the time per patient which in house helps them to prioritize patients. © 2022 IEEE.