Skin Disease Detection System Technologies Using Image Processing
Abhishek Verma, Mr. Peeyush Kumar Pathak
skin disease, image processing technologies, convolutional neural networks (CNNs), disease diagnostics
Skin diseases affect a significant portion of the global population, necessitating timely and accurate diagnosis for effective treatment. Recent advancements in image processing technologies have facilitated the development of automated skin disease detection systems, offering potential improvements in diagnostic accuracy and accessibility. This paper provides a comprehensive review of various image processing techniques employed in skin disease detection, including preprocessing methods, feature extraction algorithms, and classification techniques. Key methodologies such as convolutional neural networks (CNNs), support vector machines (SVMs), and k-nearest neighbors (KNNs) are examined for their roles in enhancing image analysis. The integration of machine learning and deep learning frameworks is discussed, highlighting their contributions to increasing diagnostic precision. Challenges related to image quality, dataset diversity, and computational efficiency are also addressed. The review underscores the transformative impact of image processing technologies in dermatology, paving the way for robust, non-invasive, and scalable skin disease detection systems. Future research directions are proposed to further refine these technologies and ensure their widespread clinical adoption.
Article Details
Unique Paper ID: 165860

Publication Volume & Issue: Volume 11, Issue 1

Page(s): 1885 - 1893
Article Preview & Download

Share This Article

Join our RMS

Conference Alert

NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024

Last Date: 15th March 2024

Call For Paper

Volume 11 Issue 1

Last Date for paper submitting for Latest Issue is 25 June 2024

About Us enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on

Social Media

Google Verified Reviews