Automatic Lesion Detection System (ALDS) for Skin Cancer Classification Using SVM and Neural Classifiers
2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE) (2016)
Oct. 31, 2016 to Nov. 2, 2016
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/BIBE.2016.53
Technology aided platforms provide reliable tools in almost every field these days. These tools being supported by computational power are significant for applications that need sensitive and precise data analysis. One such important application in the medical field is Automatic Lesion Detection System (ALDS) for skin cancer classification. Computer aided diagnosis helps physicians and dermatologists to obtain a "second opinion" for proper analysis and treatment of skin cancer. Precise segmentation of the cancerous mole along with surrounding area is essential for proper analysis and diagnosis. This paper is focused towards the development of improved ALDS framework based on probabilistic approach that initially utilizes active contours and watershed merged mask for segmenting out the mole and later SVM and Neural Classifier are applied for the classification of the segmented mole. After lesion segmentation, the selected features are classified to ascertain that whether the case under consideration is melanoma or non-melanoma. The approach is tested for varying datasets and comparative analysis is performed that reflects the effectiveness of the proposed system.
Lesions, Malignant tumors, Support vector machines, Algorithm design and analysis, Skin cancer, Active contours, Image segmentation
M. A. Farooq, M. A. Azhar and R. H. Raza, "Automatic Lesion Detection System (ALDS) for Skin Cancer Classification Using SVM and Neural Classifiers," 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE), Taichung, Taiwan, 2016, pp. 301-308.