A multiple classifier system for early melanoma diagnosis

Sboner, Andrea; Eccher, Claudio; Blanzieri, Enrico; Bauer, P.; Cristofolini, Mario; Zumiani, Giuseppe; Forti, Stefano

doi:10.1016/s0933-3657(02)00087-8

Cited by 109 publications

(64 citation statements)

References 21 publications

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“…Accuracy of the model is stable over much of the parameter range, with lower accuracy only occurring where lambda forces few to no features to be included in the model. Dundar et al, 2011Dundar et al, , 2010Esgiar et al, 2002;Farjam et al, 2007;Fatima et al, 2014;Glotsos et al, 2008;Gunduz et al, 2004;Gupta et al, 2001;Hall et al, 2008;Huang and Lai, 2010;Huang and Lee, 2009;Jafari-Khouzani and Soltanian-Zadeh, 2003;Monaco and Tomaszewski, 2008;Kong et al, 2007;Kwak et al, 2011;Land et al, 2008;Lessmann et al, 2007;Meng et al, 2010;Naik et al, 2008Naik et al, , 2007Ozolek et al, 2014;Petushi et al, 2006;Qureshi et al, 2008Qureshi et al, , 2007Sboner et al, 2003;Schnorrenberg et al, 1997;Sertel et al, 2010;Sparks and Madabhushi, 2013;Sudbø et al, 2000;Tabesh and Teverovskiy, 2006;Tabesh et al, 2007Tabesh et al, , 2005Tahir and Bouridane, 2006;Tasoulis et al, 2003;Teverovskiy et al, 2004;Thiran and Macq, 1996;Tsai et al, 2009;Xu et al 2014aXu et al , 2014bYang et al, 2009). These approache...…”

Section: Discussionmentioning

confidence: 99%

“…Even before WSI was practical, researchers captured a single frame of an entire slide using a standard microscope creating a subset of the image for computational analysis (Adiga et al, 2006;Aiad et al, 2009;Altunbay et al, 2010;Baak et al, 1981;Brook et al, 2007;Esgiar et al, 2002Esgiar et al, , 2002Farjam et al, 2007;Fatima et al, 2014;Glotsos et al, 2008;Gunduz et al, 2004;Gupta et al, 2001;Hall et al, 2008;Huang and Lai, 2010;Huang and Lee, 2009;Jafari-Khouzani and Soltanian-Zadeh, 2003;Kong et al, 2007;Land et al, 2008;Lessmann et al, 2007;Meng et al, 2010;Ozolek et al, 2014;Qureshi et al, 2008Qureshi et al, , 2007Sboner et al, 2003;Schnorrenberg et al, 1997;Sudbø et al, 2000;Tahir and Bouridane, 2006;Tasoulis et al, 2003;Thiran and Macq, 1996;Tsai et al, 2009). For many researchers doing pathology image analysis today, this is still the best option, either because they do not have access to a WSI system, or because they can capture data not possible with a WSI system, such as confocal microscopy images.…”

Section: Image Subsetting Methodsmentioning

confidence: 99%

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Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles

Barker

Hoogi

Depeursinge

et al. 2016

Medical Image Analysis

193

110

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a b s t r a c tComputerized analysis of digital pathology images offers the potential of improving clinical care (e.g. automated diagnosis) and catalyzing research (e.g. discovering disease subtypes). There are two key challenges thwarting computerized analysis of digital pathology images: first, whole slide pathology images are massive, making computerized analysis inefficient, and second, diverse tissue regions in whole slide images that are not directly relevant to the disease may mislead computerized diagnosis algorithms. We propose a method to overcome both of these challenges that utilizes a coarse-to-fine analysis of the localized characteristics in pathology images. An initial surveying stage analyzes the diversity of coarse regions in the whole slide image. This includes extraction of spatially localized features of shape, color and texture from tiled regions covering the slide. Dimensionality reduction of the features assesses the image diversity in the tiled regions and clustering creates representative groups. A second stage provides a detailed analysis of a single representative tile from each group. An Elastic Net classifier produces a diagnostic decision value for each representative tile. A weighted voting scheme aggregates the decision values from these tiles to obtain a diagnosis at the whole slide level. We evaluated our method by automatically classifying 302 brain cancer cases into two possible diagnoses (glioblastoma multiforme (N = 182) versus lower grade glioma (N = 120)) with an accuracy of 93.1 % (p << 0.001). We also evaluated our method in the dataset provided for the 2014 MICCAI Pathology Classification Challenge, in which our method, trained and tested using 5-fold cross validation, produced a classification accuracy of 100% (p << 0.001). Our method showed high stability and robustness to parameter variation, with accuracy varying between 95.5% and 100% when evaluated for a wide range of parameters. Our approach may be useful to automatically differentiate between the two cancer subtypes.

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Section: Discussionmentioning

confidence: 99%

Section: Image Subsetting Methodsmentioning

confidence: 99%

Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles

Barker

Hoogi

Depeursinge

et al. 2016

Medical Image Analysis

193

110

View full text Add to dashboard Cite

show abstract

“…In Sboner, et al [19], a novel multiple classifier system for the early diagnosis of melanoma was proposed based on the combination of different classification algorithms, which demonstrated a superior performance relatively to the use of each classifiers alone. The proposed system combines three different types of classifiers, namely, linear discriminant analysis (LDA), C4.5 decision tree and kNN classifiers, and uses 38 geometric and colourimetric features as input for the classifiers, and a voting scheme to combine the outputs of each classifiers.…”

Section: Related Studiesmentioning

confidence: 99%

Skin lesion computational diagnosis of dermoscopic images: Ensemble models based on input feature manipulation

Oliveira

Pereira

Tavares

2017

Computer Methods and Programs in Biomedicine

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Background and Objectives:The number of deaths worldwide due to melanoma has risen in recent times, in part because melanoma is the most aggressive type of skin cancer. Computational systems have been developed to assist dermatologists in early diagnosis of skin cancer, or even to monitor skin lesions. However, there still remains a challenge to improve classifiers for the diagnosis of such skin lesions. The main objective of this article is to evaluate different ensemble classification models based on input feature manipulation to diagnose skin lesions. Methods: Input feature manipulation processes are based on feature subset selections from shape properties, colour variation and texture analysis to generate diversity for the ensemble models. Three subset selection models are presented here: 1) a subset selection model based on specific feature groups, 2) a correlation-based subset selection model, and 3) a subset selection model based on feature selection algorithms. Each ensemble classification model is generated using an optimum-path forest classifier and integrated with a majority voting strategy. The proposed models were applied on a set of 1104 dermoscopic images using a crossvalidation procedure. Results: The best results were obtained by the first ensemble classification model that generates a feature subset ensemble based on specific feature groups. The skin lesion diagnosis computational system achieved 94.3% accuracy, 91.8% sensitivity and 96.7% specificity. Conclusions:The input feature manipulation process based on specific feature subsets generated the greatest diversity for the ensemble classification model with very promising results.

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“…The aim of a multiple classifier system is the take advantage of diversity between classifiers to improve the classifying accuracy while maintaining efficiency. Multiple classifier systems have been successfully implemented in numerous machine learning tasks including diagnosing melanoma [10], classifying breast lesions [11] and detecting naked bodies in images [12]. In the previous examples, combining multiple classifiers, under a suitable weighting scheme, was shown to improve performance compared to a single classifier.…”

Section: B Multiple Classifier Systemmentioning

confidence: 99%

Tuning a Multiple Classifier System for Side Effect Discovery Using Genetic Algorithms

2014

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Abstract-In previous work, a novel supervised framework implementing a binary classifier was presented that obtained excellent results for side effect discovery. Interestingly, unique side effects were identified when different binary classifiers were used within the framework, prompting the investigation of applying a multiple classifier system. In this paper we investigate tuning a side effect multiple classifying system using genetic algorithms. The results of this research show that the novel framework implementing a multiple classifying system trained using genetic algorithms can obtain a higher partial area under the receiver operating characteristic curve than implementing a single classifier. Furthermore, the framework is able to detect side effects efficiently and obtains a low false positive rate.

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A multiple classifier system for early melanoma diagnosis

Cited by 109 publications

References 21 publications

Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles

Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles

Skin lesion computational diagnosis of dermoscopic images: Ensemble models based on input feature manipulation

Tuning a Multiple Classifier System for Side Effect Discovery Using Genetic Algorithms

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