Abstract
Skin, the vital body part, can be affected by various diseases and inflammations because of numerous known and unknown bacteria, fungi, and other microorganisms. Automated skin disease detection is a potential method to reduce the cost and improve the effectiveness of skin disease detection in the initial stage. Our proposed work presents a computer-aided skin disease detection approach using an Artificial Neural Network. This work uses an image analysis technique to classify four classes of images, i.e., eczema, hemangioma, malignant melanoma, and stasis dermatitis. It takes the digital images of the disease and does the necessary preprocessing steps. Then the GLCM and HOG features are extracted from the image. An ANN model is trained with these features. The overall accuracy of our work is 93.5% and 78.1% for GLCM and HOG features, respectively. The test result of the process is shown to the user through GUI which also recommend the treatment for the initial stage.
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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Nourin, N., Kundu, P., Saima, S., Rahman, M.A. (2023). GLCM and HOG Feature-Based Skin Disease Detection Using Artificial Neural Network. In: Ahmad, M., Uddin, M.S., Jang, Y.M. (eds) Proceedings of International Conference on Information and Communication Technology for Development. Studies in Autonomic, Data-driven and Industrial Computing. Springer, Singapore. https://doi.org/10.1007/978-981-19-7528-8_28
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DOI: https://doi.org/10.1007/978-981-19-7528-8_28
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