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Full-Text Articles in Physical Sciences and Mathematics
Neural Network Diagnosis Of Malignant Melanoma From Color Images, Fikret Erçal, Hsi-Chieh Lee, William V. Stoecker, Randy Hays Moss, Anurag Chawla
Neural Network Diagnosis Of Malignant Melanoma From Color Images, Fikret Erçal, Hsi-Chieh Lee, William V. Stoecker, Randy Hays Moss, Anurag Chawla
Computer Science Faculty Research & Creative Works
Malignant melanoma is the deadliest form of all skin cancers. Approximately 32,000 new cases of malignant melanoma were diagnosed in 1991 in the United States, with approximately 80% of patients expected to survive 5 years. Fortunately, if detected early, even malignant melanoma may be treated successfully, Thus, in recent years, there has been rising interest in the automated detection and diagnosis of skin cancer, particularly malignant melanoma. Here, the authors present a novel neural network approach for the automated separation of melanoma from 3 benign categories of tumors which exhibit melanoma-like characteristics. The approach uses discriminant features, based on tumor …
Detection Of Skin Tumor Boundaries In Color Images, Fikret Erçal, M. Moganti, William V. Stoecker, Randy Hays Moss
Detection Of Skin Tumor Boundaries In Color Images, Fikret Erçal, M. Moganti, William V. Stoecker, Randy Hays Moss
Computer Science Faculty Research & Creative Works
A simple and yet effective method for finding the borders of tumors is presented as an initial step towards the diagnosis of skin tumors from their color images. The method makes use of an adaptive color metric from the red, green, and blue planes that contains information for discriminating the tumor from the background. Using this suitable coordinate transformation, the image is segmented. The tumor portion is then extracted from the segmented image and borders are drawn. Experimental results that verify the effectiveness of this approach are given