Skin disease detection using machine learning

The skin serves as the body’s primary protective barrier, safeguarding vital internal organs from external harm. However, this crucial organ is vulnerable to various infections caused by fungi, viruses, bacteria, or even environmental factors such as dust. Across the world, millions of people suffer from diverse skin disorders, ranging from common conditions like eczema and acne to more severe or contagious diseases. In some cases, even a minor lesion can escalate into a serious medical issue if left untreated. Certain skin infections are highly transmissible and can spread through simple contact, such as a handshake or sharing personal items. Accurate and timely diagnosis plays a vital role in ensuring effective treatment and raising awareness about these conditions. In this project, we utilize Convolutional Neural Networks (CNNs) to detect and classify skin diseases from medical images. CNNs have demonstrated remarkable success in image recognition and classification tasks, making them highly suitable for dermatological analysis. For training purposes, a skin disease dataset containing nine distinct categories—Actinic Keratosis, Basal Cell Carcinoma, Dermatofibroma, Melanoma, Nevus, Pigmented Benign Keratosis, Seborrheic Keratosis, Squamous Cell Carcinoma, and Vascular Lesion—was used.