Melanoma Skin Cancer Detection Using Different Machine Learning Technique
Keywords:
Melanoma, Dermatoscope, CNN, SVM, Preprocessing, Diagnosis, Feature Extraction, skin lesion identification, segmentation, ABCDE, handcrafted, color, texture feature, classifierAbstract
Expanded rate of skin cancer is vast. Melanoma is one of the most increased cancers since past decades. It should be detected early because of its aggressiveness. To diagnose melanoma earlier, skin lesion should be segmented correctly and characterize the benign and malignant cases. In this study, we combine handcrafted and automatic feature of CNN to generate the high classification accuracy with all CNN layers and combining integrated approach of CNN, sparse coding and Neural Network and K-Nearest Neighbor to identify melanoma and classify and plot it using principal component analysis algorithm and classified the melanoma skin lesion and train and test the skin lesion image and evaluate the skin lesion sensitivity, specificity, accuracy and generate the ROC Curve. The outcome of tentative evaluation proves that using ISIC, ISBI, VGG database to achieve 97% accuracy, 96% specificity and 95% sensitivity.
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