Automatic Lesion Detection System (ALDS) for Skin Cancer Classification Using SVM and Neural Classifiers
This work addresses the problem of precise skin cancer diagnosis for physicians and dermatologists, but it is incremental as it builds on existing ALDS frameworks with hybrid methods.
The paper tackled skin cancer classification by developing an improved Automatic Lesion Detection System (ALDS) using a probabilistic approach with active contours and watershed merged mask for segmentation, followed by SVM and neural classifiers for classification, achieving effectiveness as shown in comparative analysis on varying datasets.
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.