Volume no :
8 |
Issue no :
1
Article Type :
Google Scholar
Author :
Dr R Senthilkumar, Linjurajan, Boppana Pravallika
Published Date :
Oct, 12, 2024
Publisher :
Journal of Artificial Intelligence and Cyber Security (JAICS)
Page No: 40 - 47
Abstract : The AI-based tool for preliminary diagnosis of dermatological manifestations offers a promising solution to enhance early detection and improve patient care by utilizing advanced artificial intelligence techniques. This tool employs deep learning algorithms, particularly convolutional neural networks (CNNs), to analyze images of skin conditions captured through smartphones or digital devices. Trained on extensive datasets annotated by expert dermatologists, the system can identify and classify a wide range of skin disorders such as acne, eczema, psoriasis, melanoma, and other benign and malignant lesions. The process begins with image preprocessing to enhance quality and segment affected areas, followed by feature extraction and classification by the AI model. The tool then provides a preliminary diagnosis along with a confidence score, assisting users and healthcare professionals in making timely decisions regarding further medical evaluation. This approach addresses critical challenges such as the shortage of dermatologists, especially in remote or underserved regions, by enabling accessible and cost-effective skin health screening. Additionally, the user- friendly interface encourages self-monitoring and early consultation, crucial for conditions where early diagnosis significantly affects treatment outcomes. Continuous learning and updates to the AI model improve diagnostic accuracy over time. The system also incorporates strict data privacy and ethical standards to ensure patient confidentiality and reduce algorithmic bias. Validation studies demonstrate that the AI tool achieves performance comparable to expert dermatologists in many cases, making it a valuable adjunct in clinical settings. In conclusion, this AI-driven preliminary diagnostic tool has the potential to revolutionize dermatological care by providing scalable, efficient, and accurate skin disease screening solutions.
Keyword Artificial intelligence, Dermatological diagnosis, Deep learning, Skin disease classification, Preliminary screening, Computer vision
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