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Citation

Ali A & Deserno TM (2012) A Systematic Review of Automated Melanoma Detection in Dermatoscopic Images and its Ground Truth Data. In: Abbey C & Mello-Thoms C (eds.) Medical Imaging 2012: Image Perception, Observer Performance, and Technology Assessment. SPIE Proceedings, 8318. SPIE Medical Imaging 2012, San Diego, CA, USA, 04.02.2012-09.02.2012. Bellingham, WA, USA: SPIE. https://www.spiedigitallibrary.org/conference-proceedings-of-spie/8318/1/A-systematic-review-of-automated-melanoma-detection-in-dermatoscopic-images/10.1117/12.912389.pdf?SSO=1; https://doi.org/10.1117/12.912389

Abstract
Malignant melanoma is the third most frequent type of skin cancer and one of the most malignant tumors, accounting for 79% of skin cancer deaths. Melanoma is highly curable if diagnosed early and treated properly as survival rate varies between 15% and 65% from early to terminal stages, respectively. So far, melanoma diagnosis is depending subjectively on the dermatologist's expertise. Computer-aided diagnosis (CAD) systems based on epiluminescense light microscopy can provide an objective second opinion on pigmented skin lesions (PSL). This work systematically analyzes the evidence of the effectiveness of automated melanoma detection in images from a dermatoscopic device. Automated CAD applications were analyzed to estimate their diagnostic outcome. Searching online databases for publication dates between 1985 and 2011, a total of 182 studies on dermatoscopic CAD were found. With respect to the systematic selection criterions, 9 studies were included, published between 2002 and 2011. Those studies formed databases of 14,421 dermatoscopic images including both malignant "melanoma" and benign "nevus", with 8,110 images being available ranging in resolution from 150 x 150 to 1568 x 1045 pixels. Maximum and minimum of sensitivity and specificity are 100.0% and 80.0% as well as 98.14% and 61.6%, respectively. Area under the receiver operator characteristics (AUC) and pooled sensitivity, specificity and diagnostics odds ratio are respectively 0.87, 0.90, 0.81, and 15.89. So, although that automated melanoma detection showed good accuracy in terms of sensitivity, specificity, and AUC, but diagnostic performance in terms of DOR was found to be poor. This might be due to the lack of dermatoscopic image resources (ground truth) that are needed for comprehensive assessment of diagnostic performance. In future work, we aim at testing this hypothesis by joining dermatoscopic images into a unified database that serves as a standard reference for dermatology related research in PSL classification.