Indian journal of dermatology · 2026
Exploring AI as a Diagnostic Tool in Medical Imaging for Dermatopathological Diseases.
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Kakada Pravallika, Ramani Pratibha, Rajinikanth Venkatesan, Janaki V R
Аннотация
BACKGROUND: Oral health is critical to overall well-being, with abnormalities leading to various health issues. Pemphigus is an autoimmune disease characterized by painful blisters on the skin and mucous membranes, necessitating accurate diagnosis and timely treatment.
OBJECTIVE: This study aims to develop an artificial intelligence (AI) tool utilizing deep learning techniques for the detection of pemphigus from clinical and histological images, enhancing diagnostic accuracy.
METHODS: A supervised learning approach was employed, utilizing a pre-trained MobileNet deep learning model to classify histology images into healthy and pemphigus categories. The dataset was prepared through cropping, resizing, and labeling of images.
RESULTS: The MobileNet model achieved an accuracy of over 92% in distinguishing between normal mucosa and pemphigus lesions. The proposed AI tool can serve as an initial diagnostic check, facilitating faster treatment planning.
CONCLUSION: The implementation of an AI-based diagnostic tool for pemphigus detection demonstrates significant potential in reducing the diagnostic burden in clinical settings. Future work will focus on real-time disease detection applications.
OBJECTIVE: This study aims to develop an artificial intelligence (AI) tool utilizing deep learning techniques for the detection of pemphigus from clinical and histological images, enhancing diagnostic accuracy.
METHODS: A supervised learning approach was employed, utilizing a pre-trained MobileNet deep learning model to classify histology images into healthy and pemphigus categories. The dataset was prepared through cropping, resizing, and labeling of images.
RESULTS: The MobileNet model achieved an accuracy of over 92% in distinguishing between normal mucosa and pemphigus lesions. The proposed AI tool can serve as an initial diagnostic check, facilitating faster treatment planning.
CONCLUSION: The implementation of an AI-based diagnostic tool for pemphigus detection demonstrates significant potential in reducing the diagnostic burden in clinical settings. Future work will focus on real-time disease detection applications.