DermaAI: An AI-Powered Skin Disease Detection System Using Deep Learning

Skin diseases represent a vast proportion of primary care visits, and their prompt and correct diagnosis may necessitate

expertise on the side of a specialist. Artificial intelligence has potential to help screening skin diseases through analyzing skin

lesion images. In this paper, the author will discuss the implementation of DermaAI, a multi-class skin disease detection system

based on integrated deep-learning. We have prepared a source labeled dataset of dermatological pictures (cz/dermatitis,

psoriasis, fungi, acne, rosacea, and vitiligo) and trained an EfficientNetB0 convolutional neural network (CNN) with transfer

learning. This model was trained in two stages: one with training the newly-added layers only, and second fine-tuning the entire

network, and the methods include data augmentation, class re-sampling, and learning rate adjustment using the form of a

callback. It was implemented through an offline inference application using TensorFlow Lite and a Flask web interface and

Android application. DermaAI has high accuracy (around 92 percent on a held-out test set) on classifying the target conditions.

An error analysis (with confusion matrix) indicates that the error rate is strong on all classes with most confusion made among

clinically similar categories. The system gives probabilities per class to help with the estimation of confidence. These findings

indicate that DermaAI may be used as an effective dermatological tool, particularly in resource-constrained environments with a

low number of specialists available[1], [2].