ARCHIVES

Skin disorders represent a significant global health concern, affecting millions of people across diverse age groups and regions. Early detection and accurate diagnosis are critical, as delays may lead to complications, higher treatment costs, and reduced quality of life. Traditionally, dermatologists rely on manual visual inspection, which, although effective, often suffers from subjectivity, inconsistencies, and time constraints. With increasing patient loads and the complexity of cases, there is a growing demand for automated, reliable, and scalable diagnostic solutions.This project introduces DermaAI, an artificial intelligence-based approach for automated skin disorder detection. Leveraging the power of Convolutional Neural Networks (CNNs), the system is trained on large and diverse skin image datasets to classify common conditions such as eczema, psoriasis, acne, and melanoma. The model is designed with preprocessing steps like image resizing, normalization, and augmentation to enhance robustness and accuracy. Evaluation metrics such as accuracy, precision, recall, and F1-score are employed to validate the system’s effectiveness and ensure clinical reliability.In addition to enhancing diagnostic accuracy, the system promotes large-scale screening capabilities and ensures digital record-keeping for patient follow-ups. This dual advantage of automation and accessibility has the potential to transform dermatological care by facilitating early detection and timely intervention, ultimately improving patient outcomes. The work demonstrates how deep learning and AI-driven tools can bridge gaps in medical accessibility, making dermatology more efficient, scalable, and patient-centric