Skin Disease Classification using Deep Learning
Skin diseases pose a significant health concern worldwide, affecting millions of individuals. The accurate and timely diagnosis of these conditions is critical for effective treatment. This project presents a robust solution for skin disease classification using deep learning techniques, specifically the VGG16 architecture, implemented in MATLAB. The primary objective of this research is to develop a highly accurate and efficient model for the automated classification of skin diseases.
The dataset used in this project is composed of five distinct classes of skin diseases, including Acne-cystic acne, biting fleas, diabetic blisters, spider bites, and vitiligo. Each class in the dataset is carefully curated to represent a wide range of skin conditions, making the model versatile and capable of handling various dermatological challenges.
The VGG16 architecture, a well-established convolutional neural network (CNN) model, is employed for its remarkable feature extraction capabilities. Transfer learning is applied to fine-tune the pre-trained VGG16 model on the skin disease dataset. The model is trained, validated, and tested using a rigorous cross-validation approach to ensure its reliability.
One of the standout achievements of this project is the exceptional classification accuracy obtained. The model demonstrates an impressive accuracy of 98.08%, signifying its effectiveness in accurately identifying and classifying skin diseases. This high accuracy rate is crucial in reducing misdiagnoses and enhancing the overall quality of patient care.
In addition to its high accuracy, the proposed system also offers real-time skin disease classification, making it a valuable tool for medical professionals and dermatologists. The user-friendly interface developed in MATLAB ensures ease of use and accessibility, allowing healthcare practitioners to make informed decisions swiftly and accurately.
In summary, this project presents a comprehensive approach to skin disease classification using deep learning techniques, with a focus on the VGG16 architecture. The achieved accuracy of 98.08% demonstrates the model’s capability to accurately classify various skin diseases, thus aiding in early diagnosis and effective treatment. This research contributes to the advancement
PROJECT OUTPUT VIDEO:
ALGORITHM / MODEL USED:
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- Monitor : 15’’ LED.
- Input Devices : Keyboard, Mouse.
- Ram : 8 GB.
- Operating system : Windows 10 Pro.
- Coding Language : MATLAB
- Tool : MATLABR2023B
Md. Nazmul Hossen; Vijayakumari Panneerselvam; Deepika Koundal; Kawsar Ahmed, Francis M. Bui, “Federated Machine Learning for Detection of Skin Diseases and Enhancement of Internet of Medical Things (IoMT) Security”, IEEE Journal of Biomedical and Health Informatics ( Volume: 27, Issue: 2, February 2023), IEEE 2023.