Automatic Classification of Specific Melanocytic Lesions Using Artificial Intelligence

Abstract

Background. Given its propensity to metastasize, and lack of effective therapies for most patients with advanced disease, early detection of melanoma is a clinical imperative. Different computer-aided diagnosis (CAD) systems have been proposed to increase the specificity and sensitivity of melanoma detection. Although such computer programs are developed for different diagnostic algorithms, to the best of our knowledge, a system to classify different melanocytic lesions has not been proposed yet. Method. In this research we present a new approach to the classification of melanocytic lesions. This work is focused not only on categorization of skin lesions as benign or malignant but also on specifying the exact type of a skin lesion including melanoma, Clark nevus, Spitz/Reed nevus, and blue nevus. The proposed automatic algorithm contains the following steps: image enhancement, lesion segmentation, feature extraction, and selection as well as classification. Results. The algorithm has been tested on 300 dermoscopic images and achieved accuracy of 92% indicating that the proposed approach classified most of the melanocytic lesions correctly. Conclusions. A proposed system can not only help to precisely diagnose the type of the skin mole but also decrease the amount of biopsies and reduce the morbidity related to skin lesion excision.

eSkin: Study on the Smartphone Application for Early Detection of Malignant Melanoma

“…The innovation is firstly based on the differentiation between micromelanomas which have diameter less than 5 mm and advanced skin moles. Secondly, we propose an additional algorithm for the skin-lesion differentiation that can be applied in a teledermoscopy system and has been described in our work [25]. In this section, we present the system overview and describe the technical requirements, application architecture, and behavioral and dynamic aspects of the system.…”

“…Separation of individual changes allows us to obtain a more accurate and reliable computer diagnosis system. The Micro-Melanoma algorithm and the Specific Melanocytic Lesion algorithm have been described in detail in [25,28].…”

Recognition of rotor damages in a DC motor using acoustic signals

“…For this reason, the step of classification could be selected in various ways. Most of the obtained feature vectors were separable linearly, therefore many classification methods could be used to solve problems such as: fuzzy logic [46], clustering method [47], nearest mean, k-nearest neighbour classifier [36,48,49], neural network [50][51][52][53][54][55], naive Bayes classifier [56], classifier based on word coding [36], linear discriminant analysis (LDA) [57,58], support vector machine [59,60], rules based on the theory of rough sets [61], Gaussian mixture models (GMM) [62,63]. The authors decided to analyse LDA, nearest neighbour (NN) classifier, and the nearest mean (NM) classi- Fig.…”

Fault diagnostics of DC motor using acoustic signals and MSAF-RATIO30-EXPANDED

“…The problem of classification was already discussed in literature [29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45]. Neural networks were described in many scientific articles [37][38][39][40][41][42].…”