International Journal of Advanced Research in Computer and Communication Engineering

Abstract: The Project named “SKINSCAN - Disease Detection”, a skin-detecting method used to detect skin disease type and its accuracy.
This project focuses on using advanced deep learning techniques to accurately classify different skin diseases. It uses a specific model called VGG16, which is great at analysing images. The goal is to develop a reliable model that can automatically identify skin diseases from images, making diagnosis faster and more accurate. -More

The dataset used in this project includes five types of skin conditions: Acne-cystic acne, biting fleas, diabetic blisters, spider bites, and vitiligo. The model is designed to recognize these different conditions, ensuring it can handle a variety of skin problems. By using a technique called transfer learning, the pre-trained VGG16 model is finetuned to work with the skin disease dataset. The model goes through extensive training, validation, and testing to ensure it is highly accurate.
One of the key successes of this project is the model's high accuracy rate of 98.08%. This means it can correctly identify skin diseases in most cases, which is important for reducing incorrect diagnoses and improving patient care. Additionally, the system can classify skin diseases in real- time, making it a useful tool for doctors and dermatologists. The user interface, created in MATLAB, is designed to be easy to use, allowing healthcare professionals to quickly and accurately make decisions.

Overall, this project provides a comprehensive solution for skin disease classification using deep learning, achieving high accuracy and aiding in early diagnosis and effective treatment.

Keywords: Automated Skin Disease Diagnosis, Deep Convolutional Neural Networks, Transfer Learning–Based Classification, dermoscopic Image Analysis, Data Augmentation and Preprocessing, Multiclass Skin Lesion Recognition, Performance Metrics Evaluation, Clinical Decision Support Systems.

Downloads: | DOI: 10.17148/IJARCCE.2025.1412118