We are analyzing https://link.springer.com/article/10.1007/s10916-019-1400-8.

Title:
Detection of Skin Cancer Using SVM, Random Forest and kNN Classifiers | Journal of Medical Systems
Description:
Most common and deadly type of cancer is Skin cancer. The destructive kind of cancers in skin is Melanoma as well as it can be identified at the initial stage and can be cured completely. For the diagnosis of melanoma, the identification of the melanocytes in the area of epidermis is an essential stage. In this paper the watershed segmentation method is implemented for segmentation. The extracted segments are subjected to feature extraction. The features extracted are shape, ABCD rule and GLCM. The extracted features are then used for classification. The classifiers are kNN (k Nearest Neighbor), Random Forest and SVM (Support Vector Machine). Among different classifiers, the SVM classifier provided better results for the skin lesions classification.
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Keywords {🔍}

article, skin, melanoma, google, scholar, detection, cancer, journal, segmentation, images, svm, classification, international, conference, privacy, cookies, content, data, information, research, classifiers, image, ieee, computer, publish, search, systems, knn, nair, kumar, diagnosis, access, lesion, chapter, springer, analysis, processing, log, medical, random, forest, published, murugan, sanu, extracted, features, lesions, malignant, discover, methods,

Topics {✒️}

month download article/chapter fuzzy-based data transformation abcd rule privacy choices/manage cookies image thresholding based skin melanoma segmentation international research journal dermatological photographs skin cancer diagnosis image segmen- tation full article pdf support vector machine melanoma” international conference watershed segmentation method medical systems aims increase classification performance skin lesions classification scope submit manuscript josephin arockia dhivya malignant melanoma images contour structural irregularity multistage illumination modeling illumination-corrected lesion �implementing dewa framework free online sources pigmented skin lesions conditions privacy policy type-2 fuzzy logic additional information publisher svm classifier provided skin cancer accepting optional cookies european economic area related subjects natural computing approaches ethics declarations conflict journal finder publish computer vision techniques ieee international conference check access instant access automatic detection skin melanoma malignant melanoma sanal kumar springer-verlag personal data published maps skin lesions article journal

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      headline:Detection of Skin Cancer Using SVM, Random Forest and kNN Classifiers
      description:Most common and deadly type of cancer is Skin cancer. The destructive kind of cancers in skin is Melanoma as well as it can be identified at the initial stage and can be cured completely. For the diagnosis of melanoma, the identification of the melanocytes in the area of epidermis is an essential stage. In this paper the watershed segmentation method is implemented for segmentation. The extracted segments are subjected to feature extraction. The features extracted are shape, ABCD rule and GLCM. The extracted features are then used for classification. The classifiers are kNN (k Nearest Neighbor), Random Forest and SVM (Support Vector Machine). Among different classifiers, the SVM classifier provided better results for the skin lesions classification.
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