Bagged textural and color features for melanoma skin cancer detection in dermoscopic and standard images

Alfed, Naser; Khelifi, Fouad

doi:10.1016/j.eswa.2017.08.010

Cited by 72 publications

(28 citation statements)

References 38 publications

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“…Alfed and Khelifi [35] proposed a technique for detecting the types of skin cancers from dermoscopic images. Numerous mechanised methods have been proposed to determine and arrange infections to have agreeable skin disease location execution.…”

Section: Related Workmentioning

confidence: 99%

New Auxiliary Function with Properties in Nonsmooth Global Optimization for Melanoma Skin Cancer Segmentation

Abdulhamid

Şahiner

Rahebi

2020

BioMed Research International

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In this paper, an algorithm is introduced to solve the global optimization problem for melanoma skin cancer segmentation. The algorithm is based on the smoothing of an auxiliary function that is constructed using a known local minimizer and smoothed by utilising Bezier curves. This function achieves all filled function properties. The proposed optimization method is applied to find the threshold values in melanoma skin cancer images. The proposed algorithm is implemented on PH2, ISBI2016 challenge, and ISBI 2017 challenge datasets for melanoma segmentation. The results show that the proposed algorithm exhibits high accuracy, sensitivity, and specificity compared with other methods.

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Section: Related Workmentioning

confidence: 99%

New Auxiliary Function with Properties in Nonsmooth Global Optimization for Melanoma Skin Cancer Segmentation

Abdulhamid

Şahiner

Rahebi

2020

BioMed Research International

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“…The use of high quality methods at this stage will improve the performance of subsequent steps in computerized analysis systems. DullRazor (Lee, Ng, Gallagher, Coldman, & McLean, ) is the first and widely used method to remove thick hairs from the dermoscopic images, was proposed in 1997, which is later on used by (Alfed & Khelifi, ; Arora, Dubey, & Jaffery, ; Majtner, Lidayova, Yildirim‐Yayilgan, & Hardeberg, ; Victor & Ghalib, ). Kiani and Sharafat () proposed an improved and efficient method, that can remove both, the thick and thin hairs more effectively than the DullRazor.…”

Section: Related Workmentioning

confidence: 99%

An improved strategy for skin lesion detection and classification using uniform segmentation and feature selection based approach

Nasir

Khan

Sharif

et al. 2018

Microscopy Res & Technique

150

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Melanoma is the deadliest type of skin cancer with highest mortality rate. However, the annihilation in early stage implies a high survival rate therefore, it demands early diagnosis. The accustomed diagnosis methods are costly and cumbersome due to the involvement of experienced experts as well as the requirements for highly equipped environment. The recent advancements in computerized solutions for these diagnoses are highly promising with improved accuracy and efficiency. In this article, we proposed a method for the classification of melanoma and benign skin lesions. Our approach integrates preprocessing, lesion segmentation, features extraction, features selection, and classification. Preprocessing is executed in the context of hair removal by DullRazor, whereas lesion texture and color information are utilized to enhance the lesion contrast. In lesion segmentation, a hybrid technique has been implemented and results are fused using additive law of probability. Serial based method is applied subsequently that extracts and fuses the traits such as color, texture, and HOG (shape). The fused features are selected afterwards by implementing a novel Boltzman Entropy method. Finally, the selected features are classified by Support Vector Machine. The proposed method is evaluated on publically available data set PH2. Our approach has provided promising results of sensitivity 97.7%, specificity 96.7%, accuracy 97.5%, and F-score 97.5%, which are significantly better than the results of existing methods available on the same data set. The proposed method detects and classifies melanoma significantly good as compared to existing methods.

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“…Color features play a crucial role in diagnosing skin cancer disease. Alfed et al [17] used combined textural and color features for skin cancer diagnosis. Ritesh et al [18] used seven different color texture features and k-means clustering for lesion segmentation.…”

Section: Literature Surveymentioning

confidence: 99%

Performance Analysis of Low-Level and High-Level Intuitive Features for Melanoma Detection

et al. 2019

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This paper presents an intelligent approach for the detection of Melanoma—a deadly skin cancer. The first step in this direction includes the extraction of the textural features of the skin lesion along with the color features. The extracted features are used to train the Multilayer Feed-Forward Artificial Neural Networks. We evaluate the trained networks for the classification of test samples. This work entails three sets of experiments including 50 % , 70 % and 90 % of the data used for training, while the remaining 50 % , 30 % , and 10 % constitute the test sets. Haralick’s statistical parameters are computed for the extraction of textural features from the lesion. Such parameters are based on the Gray Level Co-occurrence Matrices (GLCM) with an offset of 2 , 4 , 8 , 12 , 16 , 20 , 24 and 28, each with an angle of 0 , 45 , 90 and 135 degrees, respectively. In order to distill color features, we have calculated the mean, median and standard deviation of the three color planes of the region of interest. These features are fed to an Artificial Neural Network (ANN) for the detection of skin cancer. The combination of Haralick’s parameters and color features have proven better than considering the features alone. Experimentation based on another set of features such as Asymmetry, Border irregularity, Color and Diameter (ABCD) features usually observed by dermatologists has also been demonstrated. The ‘D’ feature is however modified and named Oblongness. This feature captures the ratio between the length and the width. Furthermore, the use of modified standard deviation coupled with ABCD features improves the detection of Melanoma by an accuracy of 93.7 %

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Bagged textural and color features for melanoma skin cancer detection in dermoscopic and standard images

Abstract: Bagged textural and color features for melanoma skin cancer detection in dermoscopic and standard images. Expert Systems with Applications, 90. pp. 101-110.

Cited by 72 publications

References 38 publications

New Auxiliary Function with Properties in Nonsmooth Global Optimization for Melanoma Skin Cancer Segmentation

New Auxiliary Function with Properties in Nonsmooth Global Optimization for Melanoma Skin Cancer Segmentation

An improved strategy for skin lesion detection and classification using uniform segmentation and feature selection based approach

Performance Analysis of Low-Level and High-Level Intuitive Features for Melanoma Detection

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