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- W4319993344 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." @default.
- W4319993344 created "2023-02-11" @default.
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- W4319993344 date "2023-01-01" @default.
- W4319993344 modified "2023-09-27" @default.
- W4319993344 title "GLCM and HOG Feature-Based Skin Disease Detection Using Artificial Neural Network" @default.
- W4319993344 cites W2604605309 @default.
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- W4319993344 doi "https://doi.org/10.1007/978-981-19-7528-8_28" @default.
- W4319993344 hasPublicationYear "2023" @default.
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