A decision support system for the diagnosis of melanoma: A comparative approach

Ruíz, Daniel; Berenguer, V.; Soriano, Antonio; Sánchez, Belén Ríos

doi:10.1016/j.eswa.2011.05.079

Cited by 98 publications

(61 citation statements)

References 12 publications

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“…Many previous studies have presented very high prediction rates, such as 100% in Ruiz et al (2011); however, such prediction rates were obtaining by testing the constructed classification models using internal evaluation approaches, that have been shown to give optimistically biased estimates (Ambroise, et al, 2002). These estimates may be indicative of the model's performances on the training data; however, these estimates are far from being representative of the effectiveness of the model to new, unseen data.…”

Section: Discussionmentioning

confidence: 83%

“…The first category consists of efforts focusing on statistical pattern recognition, whereas the second category comprises efforts focusing on template matching and content-based image retrieval. Regarding the first category, representative studies may be found in (Cavalcanti, et al, 2013), which proposed a k-nearest neighbor (k-NN) classifier using 52 features extracted based on the ABCD rule with 99.3% overall accuracy, in Jaleel et al (2012), which proposed an artificial neural network (ANN) classifier with 100% prediction accuracy and in Ruiz et al (2011), which proposed an ensemble pattern recognition scheme combining three distinct classifiers, the k-NN, the Bayesian and the ANN, with accuracy 87.76%. Regarding the second category, representative studies can be found in Ballerini et al (2010;2013), which proposed a content-based image retrieval system investigating textural and color features, in Maragoudakis and Maglogiannis (2011), which proposed an ontology structure model based on features extracted from skin lesion images based on agglomerative clustering and distance criteria and in Chen et al (2016), which is a recent study proposing a content-based image retrieval system that identified melanomas on plain photography images with performances exceeding 90% for all metrics tested.…”

Section: Discussionmentioning

confidence: 99%

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An Ensemble Template Matching and Content-Based Image Retrieval Scheme Towards Early Stage Detection of Melanoma

et al. 2016

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Malignant melanoma represents the most dangerous type of skin cancer. In this study we present an ensemble classification scheme, employing the mutual information, the cross-correlation and the clustering based on proximity of image features methods, for early stage assessment of melanomas on plain photography images. The proposed scheme performs two main operations. First, it retrieves the most similar, to the unknown case, image samples from an available image database with verified benign moles and malignant melanoma cases. Second, it provides an automated estimation regarding the nature of the unknown image sample based on the majority of the most similar images retrieved from the available database. Clinical material comprised 75 melanoma and 75 benign plain photography images collected from publicly available dermatological atlases. Results showed that the ensemble scheme outperformed all other methods tested in terms of accuracy with 94.9 ± 1.5%, following an external cross-validation evaluation methodology. The proposed scheme may benefit patients by providing a second opinion consultation during the self-skin examination process and the physician by providing a second opinion estimation regarding the nature of suspicious moles that may assist towards decision making especially for ambiguous cases, safeguarding, in this way from potential diagnostic misinterpretations.

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Section: Discussionmentioning

confidence: 83%

Section: Discussionmentioning

confidence: 99%

An Ensemble Template Matching and Content-Based Image Retrieval Scheme Towards Early Stage Detection of Melanoma

et al. 2016

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“…These methods work independently and also in combination making a collaborative decision support system. The classification rates obtained are around 87% [18].…”

Section: Related Workmentioning

confidence: 91%

Automatic Detection of Melanoma Skin Cancer using Texture Analysis

Mabrouk¹,

Sheha²,

Sharawy³

2012

IJCA

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Melanoma is considered the most dangerous type of skin cancer. Early and accurate diagnosis depends mainly on important issues, accuracy of feature extracted and efficiency of classifier method. This paper presents an automated method for melanoma diagnosis applied on a set of dermoscopy images. Features extracted are based on gray level Co-occurrence matrix (GLCM) and Using Multilayer perceptron classifier (MLP) to classify between Melanocytic Nevi and Malignant melanoma. MLP classifier was proposed with two different techniques in training and testing process: Automatic MLP and Traditional MLP. Results indicated that texture analysis is a useful method for discrimination of melanocytic skin tumors with high accuracy. The first technique, Automatic iteration counter is faster but the second one, Default iteration counter gives a better accuracy, which is 100 % for the training set and 92 % for the test set.

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“…Other approach for decision support tool was proposed by Daniel Ruiz et al in 2011 [20]. This independently working method includes artificial neural network (ANN) classifiers, a Bayesian classifier, and the algorithm of the K-nearest neighbours.…”

Section: Introductionmentioning

confidence: 99%

Application of automatic statistical post-processing method for analysis of ultrasonic and digital dermatoscopy images

Drulytė

Ruzgas

Raišutis

et al. 2018

Libyan Journal of Medicine

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Ultrasonic and digital dermatoscopy diagnostic methods are used in order to estimate the changes of structure, as well as to non-invasively measure the changes of parameters of lesions of human tissue. These days, it is very actual to perform the quantitative analysis of medical data, which allows to achieve the reliable early-stage diagnosis of lesions and help to save more lives. The proposed automatic statistical post-processing method based on integration of ultrasonic and digital dermatoscopy measurements is intended to estimate the parameters of malignant tumours, measure spatial dimensions (e.g. thickness) and shape, and perform faster diagnostics by increasing the accuracy of tumours differentiation. It leads to optimization of time-consuming analysis procedures of medical images and could be used as a reliable decision support tool in the field of dermatology.

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A decision support system for the diagnosis of melanoma: A comparative approach

Cited by 98 publications

References 12 publications

An Ensemble Template Matching and Content-Based Image Retrieval Scheme Towards Early Stage Detection of Melanoma

An Ensemble Template Matching and Content-Based Image Retrieval Scheme Towards Early Stage Detection of Melanoma

Automatic Detection of Melanoma Skin Cancer using Texture Analysis

Application of automatic statistical post-processing method for analysis of ultrasonic and digital dermatoscopy images

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