Comparison of computer systems and ranking criteria for automatic melanoma detection in dermoscopic images

Abstract

Melanoma is the deadliest form of skin cancer, and early detection is crucial for patient survival. Computer systems can assist in melanoma detection, but are not widespread in clinical practice. In 2016, an open challenge in classification of dermoscopic images of skin lesions was announced. A training set of 900 images with corresponding class labels and semi-automatic/manual segmentation masks was released for the challenge. An independent test set of 379 images, of which 75 were of melanomas, was used to rank the participants. This article demonstrates the impact of ranking criteria, segmentation method and classifier, and highlights the clinical perspective. We compare five different measures for diagnostic accuracy by analysing the resulting ranking of the computer systems in the challenge. Choice of performance measure had great impact on the ranking. Systems that were ranked among the top three for one measure, dropped to the bottom half when changing performance measure. Nevus Doctor, a computer system previously developed by the authors, was used to participate in the challenge, and investigate the impact of segmentation and classifier. The diagnostic accuracy when using an automatic versus the semi-automatic/manual segmentation is investigated. The unexpected small impact of segmentation method suggests that improvements of the automatic segmentation method w.r.t. resemblance to semi-automatic/manual segmentation will not improve diagnostic accuracy substantially. A small set of similar classification algorithms are used to investigate the impact of classifier on the diagnostic accuracy. The variability in diagnostic accuracy for different classifier algorithms was larger than the variability for segmentation methods, and suggests a focus for future investigations. From a clinical perspective, the misclassification of a melanoma as benign has far greater cost than the misclassification of a benign lesion. For computer systems to have clinical impact, their performance should be ranked by a high-sensitivity measure.

Appling of evolutionary computing to accelerate for melanoma cancer detection

“…It is an automated methodology for melanoma determination associated with dermoscopy picture. Various elements in this techniques were chosen utilizing fisher score technique (Møllersen et al, 2017).…”

“…Messadi et al 2014It is a guidelines based classifier to isolate a melanoma. Arrangement of tests has been performed to figure the distinctive hilter kilter estimations for the digitized shading pictures of injuries (Møllersen et al, 2017).…”

“…Although computer systems can assist in melanoma detection, but they are not widespread in clinical practice (Møllersen et al, 2017). The proposed flowchart for melanoma skin cancer detection using computer assisted highly robust computer aided diagnosis model is given in Fig.…”

“…Automatic diagnosis of melanoma: A software system based on the 7-point checklist Di Leo et al (2010) It is a new explanatory framework based on ELM 7-point plan which checks the surface parameters of the sores. This is a speedier and precise method as compared to the standard ABCD criteria in the determination of melanoma (Møllersen et al, 2017).…”

Classification for Dermoscopy Images Using Convolutional Neural Networks Based on the Ensemble of Individual Advantage and Group Decision

“…CNNs are able to learn multilevel features from original data, and the extracted features are more high-level and more robust. Many researchers established a system that combined recent developments in deep learning and machine learning for skin lesion segmentation and classification [23][24][25][26][27][28]. Schaefer et al [23] segmented the area of the lesionusing an approach based on thresholding, region growingand region merging, and the extracted features are analysed in apattern classification stage.…”