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- W3193670388 abstract "Psoriasis is an autoimmune disorder which affects the skin and causes inflammation in the skin regions which leads to lesions in hand, leg, shank, and upper back regions. The objectives of the study are to compare the performance of a hybrid automated segmentation algorithm with fuzzy c-means algorithm for segmenting the hand region of psoriasis and normal subjects and to extract the handcrafted features from the segmented image and implement the classification process using SVM classifier. In this specific study, hybrid algorithms of k-means clustering and region growing have been used for segmentation. The GLCM features are extracted and classification of psoriasis and normal hand region was performed using machine learning classifier. The percentage difference of 7.9% is achieved between the normal and psoriasis subjects. The mean average temperature difference of 2.5 °C was found between the normal and psoriasis subjects. The features such as contrast and entropy exhibit the highest percentage difference of 12.8% and 9.05%, respectively, between the normal and psoriatic hand. The sensitivity, specificity, and accuracy of the classifier were found to be 83.3%, 73.3%, and 78.3%, respectively. The Proposed hybrid algorithm provides better segmentation results compared to fuzzy c-means algorithm in segmenting psoriasis hand thermal image. The thermal imaging technique can be used as screening diagnostic tool for analyzing and detecting psoriatic lesions." @default.
- W3193670388 created "2021-08-30" @default.
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- W3193670388 date "2021-08-14" @default.
- W3193670388 modified "2023-09-24" @default.
- W3193670388 title "Automated Segmentation and Classification of Psoriasis Hand Thermal Images Using Machine Learning Algorithm" @default.
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- W3193670388 doi "https://doi.org/10.1007/978-981-16-2123-9_37" @default.
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