Skin Disease Detection System Technologies Using Image Processing
Author(s):
Abhishek Verma, Mr. Peeyush Kumar Pathak
Keywords:
skin disease, image processing technologies, convolutional neural networks (CNNs), disease diagnostics
Abstract
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
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