Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms

Abstract

Image-based computer aided diagnosis systems have significant potential for screening and early detection of malignant melanoma. We review the state of the art in these systems and examine current practices, problems, and prospects of image acquisition, pre-processing, segmentation, feature extraction and selection, and classification of dermoscopic images. This paper reports statistics and results from the most important implementations reported to date. We compared the performance of several classifiers specifically developed for skin lesion diagnosis and discussed the corresponding findings. Whenever available, indication of various conditions that affect the technique's performance is reported. We suggest a framework for comparative assessment of skin cancer diagnostic models and review the results based on these models. The deficiencies in some of the existing studies are highlighted and suggestions for future research are provided.

An SVM Framework for Malignant Melanoma Detection Based on Optimized HOG Features

“…Over the past two decades or so, many researchers in the fields of computer vision and medical image analysis have been attracted to develop high performance automatic techniques for skin cancer detection from dermoscopic images [8,9]. Accurate skin lesion segmentation plays a crucial role in automated early skin cancer detection and diagnosis systems.…”

Search for Resolution Invariant Wavelet Features of Melanoma Learned by a Limited ANN Classifier

“…In the review [2] the state of the art of melanoma CAD was examined: in-vivo imaging techniques, image acquisition, pre-processing, segmentation methods, feature extraction and selection, and classification of dermoscopic images. Of high value are the indications of various conditions that affect the technique's performance.…”

“…Early diagnosis of this tumor is a lifesaving factor. Lack of specialists, too late detections and an increased melanoma morbidity rate have become a medical problem for some time and, on that account, a challange for computer assisted diagnosis (CAD) [1][2][3]. The standard therapy, biopsy, is not feasible for all the patients due to treatment costs and some health reasons.…”

Prediction of Dermoscopy Patterns for Recognition of both Melanocytic and Non-Melanocytic Skin Lesions

“…When it is identified, then the physician must follow the next level procedure. In the second-level process, the melanocytic lesion as benign, suspect or malignant The refinement of current approaches and development of new techniques [9] will help in improving the ability to diagnose skin cancer and achieving the goal of significant reduction in melanoma mortality rate. Thus, the detection of lesions by clinical rules such as ABCD and the overall appearance of color, architectural order, symmetry of pattern and homogeneity (CASH) in dermoscopy images are still challenging tasks from CAD systems.…”