Computer Aided Diagnosis System for Melanoma Lesion Detection
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Date
2018-04
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Addis Ababa University
Abstract
Detection of skin cancer in the earlier stage is very critical. Nowadays, skin cancer is seen as one
of the most hazardous form of cancers found in humans. The most common types of skin cancers
are Basal Cell Carcinoma (BCC), Squamous Cell Carcinoma (SCC) and Melanoma. Among
these melanoma is the deadliest type of skin cancer. The detection of Melanoma cancer in early
stage can be helpful to cure it. Computer vision plays an important role in medical image
diagnosis and this has been proved by many existing systems. A computer aided skin image
diagnosis system has a significant potential for screening and prognosis. It is utmost important in
countries where unaided visual diagnosis system is the practice and where there is insufficient
number of dermatologists. This thesis presents a computer aided diagnosis system for the
detection of melanoma skin cancer using a novel mathematical scheme. The proposed method
uses a holistic representation of skin color images to extract useful features for use in effective
segmentation of melanoma lesions. The segmentation scheme is preceded by a processing stage
composed of noise filtering color space transformation. Vertex component analysis and
principal component analysis are integrated to form a hybrid approach for unmixing and feature
dimension reduction. An optimized feature selection technique is also used to obtain the best
achievable performance in effectively detecting the lesions. Support vector machine (SVM)
along with geometrical and color feature threshold values are integrated to make effective
detection of melanoma lesions. The effectiveness of the developed scheme to classify lesions
into benign, suspicious and malignant melanoma is found to be promising. The proposed scheme
has been tested on images taken from standard dermoscopy image databases and achieved
96.4% sensitivity, 99.4% specificity, and 97.9% overall accuracy for pixel based classification of
melanomas while it achieved 98% sensitivity, 100% specificity, and 98.6% accuracy for image
based classification justifying it‟s great promises.
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Keywords
Computer Aided Diagnosis, Melanoma Lesion, Detection of skin cancer