Automated diagnosis of pigmented skin lesions

Abstract

Since advanced melanoma remains practically incurable, early detection is an important step toward a reduction in mortality. High expectations are entertained for a technique known as dermoscopy or epiluminescence light microscopy; however, evaluation of pigmented skin lesions by this method is often extremely complex and subjective. To obviate the problem of qualitative interpretation, methods based on mathematical analysis of pigmented skin lesions, such as digital dermoscopy analysis, have been developed. In the present study, we used a digital dermoscopy analyzer (DBDermo-Mips system) to evaluate a series of 588 excised, clinically atypical, flat pigmented skin lesions (371 benign, 217 malignant). The analyzer evaluated 48 parameters grouped into 4 categories (geometries, colors, textures and islands of color), which were used to train an artificial neural network. To evaluate the diagnostic performance of the neural network and to check it during the training process, we used the error area over the receiver operating characteristic curve. The discriminating power of the digital dermoscopy analyzer plus artificial neural network was compared with histologic diagnosis. A feature selection procedure indicated that as few as 13 of the variables were sufficient to discriminate the 2 groups of lesions, and this also ensured high generalization power. The artificial neural network designed with these variables enabled a diagnostic accuracy of about 94%. In conclusion, the good diagnostic performance and high speed in reading and analyzing lesions (real time) of our method constitute an important step in the direction of automated diagnosis of pigmented skin lesions.

A clinico-pathological study of pigmented cutaneous lesions: a one-year prospective study in a tertiary care hospital

“…In the present study, among females the most common lesion was benign melanocytic nevi 17 (54.8%) which was similar to observations of Schafer et al 7 Nevi are benign melanocytic tumours that have cosmetic significance and may act as stimulants or precursors to melanoma. 8 In male's nevus sebaceous was most common lesion 5 (38.5%) which was similar to Rubegni et al, Rajesh et al 1,9 Majority of these lesions were situated on the face 29 (65.9%) and were similar to Rajesh et al In present study, it was observed that malignant melanoma was seen in 2 cases and both were females, which was comparable to other studies. 1,10 but dissimilar to the observations of KORA survey.melanoma is an aggressive tumour, its incidence in India is uncommon when compared to the West.…”

Automatic Detection of Melanoma and Non Melanoma Skin Cancer: Using Classification Framework of Neural Network

“…Physician uses this technique for diagnosis of skin cancer more efficiency. The historical researches have proved that ELM can improve the diagnostic accuracy by 5 -30% compare to traditional imaging [2]. Furthermore, ELM has evolution and digitalize that can be used to classification with computer.…”

Software for automatic diagnostic prediction of skin clinical images based on ABCD rule

“…On the other hand, the study of Manousaki et al, (2006), has evaluated geometry, color and texture, reaching only 60.9% of sensitivity and specificity of 95.4%. Rubegni et al (2002) have evaluated geometry, color, texture and color Islands, achieving sensitivity and specificity of 94%. The method proposed by Iyatomi et al, (2008) peaked sensitivity and specificity of 86%, based on ABCD rule.…”