A computer aided diagnostic system for malignant melanomas

Skrøvseth, Stein Olav; Schopf, Thomas R.; Thon, Kevin; Zortea, Maciel; Geilhufe, Marc; Møllersen, Kajsa; Kirchesch, Herbert M.; Godtliebsen, Fred

doi:10.1109/isabel.2010.5702825

Cited by 11 publications

(7 citation statements)

References 13 publications

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“…There are various diagnostic systems proposed in literature [3][4][5][6] but as we discussed in [7] more research is required to use multiple expert systems that can be more robust and more efficient than a single classifier alone. Such systems should take into consideration the experts' advice using the labeled data along with the capability of using unlabeled data and self-advised learning process.…”

Section: Introductionmentioning

confidence: 99%

Self-supervised learning model for skin cancer diagnosis

Masood

Al-Jumaily

Anam

2015

2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)

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Automated diagnosis of skin cancer is an active area of research with different classification methods proposed so far. However, classification models based on insufficient labeled training data can badly influence the diagnosis process if there is no self-advising and semi supervising capability in the model. This paper presents a semi supervised, self-advised learning model for automated recognition of melanoma using dermoscopic images. Deep belief architecture is constructed using labeled data together with unlabeled data, and fine tuning done by an exponential loss function in order to maximize separation of labeled data. In parallel a self-advised SVM algorithm is used to enhance classification results by counteracting the effect of misclassified data. To increase generalization capability and redundancy of the model, polynomial and radial basis function based SA-SVMs and Deep network are trained using training samples randomly chosen via a bootstrap technique. Then the results are aggregated using least square estimation weighting. The proposed model is tested on a collection of 100 dermoscopic images. The variation in classification error is analyzed with respect to the ratio of labeled and unlabeled data used in the training phase. The classification performance is compared with some popular classification methods and the proposed model using the deep neural processing outperforms most of the popular techniques including KNN, ANN, SVM and semi supervised algorithms like Expectation maximization and transductive SVM.

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Section: Introductionmentioning

confidence: 99%

Self-supervised learning model for skin cancer diagnosis

Masood

Al-Jumaily

Anam

2015

2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)

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“…The latter may represent the historical evolution of the lesion in order to diagnose it, including changes in its shape, size, shades of colour, or surface features. To the best of our knowledge, few previous image analysis systems of skin lesions surveyed in the literature have used such features [127,128]. One of the reasons may be related to the complexity of feature extraction from the elevation criterion, or even the unavailability of a database with at least two images of the same lesion that must be taken over time to assess its evolution.…”

Section: Other Featuresmentioning

confidence: 99%

Computational methods for pigmented skin lesion classification in images: review and future trends

Oliveira

Papa

Pereira

et al. 2016

Neural Comput & Applic

172

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Skin cancer is considered as one of the most common types of cancer in several countries, and its incidence rate has increased in recent years. Melanoma cases have caused an increasing number of deaths worldwide, since this type of skin cancer is the most aggressive compared to other types. Computational methods have been developed to assist dermatologists in early diagnosis of skin cancer. An overview of the main and current computational methods that have been proposed for pattern analysis and pigmented skin lesion classification is addressed in this review. In addition, a discussion about the application of such methods, as well as future trends, is also provided. Several methods for feature extraction from both macroscopic and dermoscopic images and models for feature selection are introduced and discussed. Furthermore, classification algorithms and evaluation procedures are described, and performance results for lesion classification and pattern analysis are given.

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“…Although it showed quite promising results in various applications but in complex applications the search performance get highly depended on the mutation strategy, crossover operation and control factors including scale factor (F), Cross over rate (Cr) and population size (NP) [7] [14].…”

Section: Adaptive Differential Evolution Based Feature Selectionmentioning

confidence: 99%

“…2) Set the mutation parameter (F) and cross over control parameter (Cr) using the following equations Cr is initialized with vlaue of 0.6 and Gaussian distribution is used as opposite to Cauchy distribution its short tail property help in keeping the value of Cr within unity [7] which is also required here. m Cr _best is the successful crossover probability in the current generation .…”

Section: ) Initialize the Population Of Np Individuals Popmentioning

confidence: 99%

“…This paper proposes a new adaptive differential evolution algorithm based search strategy for feature selection that showed promising results for skin cancer diagnosis both for dermatological as well as histopathological image datasets. It should be noted that the control parameters and learning strategies involved in DE are highly dependent on the problem under consideration and need to be adjusted more adaptively [7]. Thus, one of the research objectives was to adaptively adjust the related control parameter and select the feature subset simultaneously for the problem under consideration, without degrading the classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Semi Advised SVM with Adaptive Differential Evolution Based Feature Selection for Skin Cancer Diagnosis

Masood¹,

Al-Jumaily²

2015

JCC

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Automated diagnosis of skin cancer is an important area of research that had different automated learning methods proposed so far. However, models based on insufficient labeled training data can badly influence the diagnosis results if there is no advising and semi supervising capability in the model to add unlabeled data in the training set to get sufficient information. This paper proposes a semi-advised support vector machine based classification algorithm that can be trained using labeled data together with abundant unlabeled data. Adaptive differential evolution based algorithm is used for feature selection. For experimental analysis two type of skin cancer datasets are used, one is based on digital dermoscopic images and other is based on histopathological images. The proposed model provided quite convincing results on both the datasets, when compared with respective state-of-the art methods used for feature selection and classification phase.

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A computer aided diagnostic system for malignant melanomas

Cited by 11 publications

References 13 publications

Self-supervised learning model for skin cancer diagnosis

Self-supervised learning model for skin cancer diagnosis

Computational methods for pigmented skin lesion classification in images: review and future trends

Semi Advised SVM with Adaptive Differential Evolution Based Feature Selection for Skin Cancer Diagnosis

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