Texture and color feature extraction for classification of melanoma using SVM

Kavitha, J.; Suruliandi, A.

doi:10.1109/icctide.2016.7725347

Cited by 54 publications

(24 citation statements)

References 11 publications

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“…Shape asymmetry mathematically models the human observation of a lesion and correlates it to the ABCD rule for lesion classification. Global and local texture characteristics, i.e., GLCM parameters and SURF features, were used for classification of melanoma by the instrumentality of SVM and KNN algorithms [ 27 ]. The authors reported an accuracy of 79.3% and 78.2% using SVM and KNN for GLCM parameters, and of 87.3% and 85.2% using SVM and KNN for SURF features, respectively, when a reduced image dataset was analyzed.…”

Section: Related Workmentioning

confidence: 99%

Towards Accurate Diagnosis of Skin Lesions Using Feedforward Back Propagation Neural Networks

et al. 2021

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In the automatic detection framework, there have been many attempts to develop models for real-time melanoma detection. To effectively discriminate benign and malign skin lesions, this work investigates sixty different architectures of the Feedforward Back Propagation Network (FFBPN), based on shape asymmetry for an optimal structural design that includes both the hidden neuron number and the input data selection. The reason for the choice of shape asymmetry was based on the 5–10% disagreement between dermatologists regarding the efficacy of asymmetry in the diagnosis of malignant melanoma. Asymmetry is quantified based on lesion shape (contour), moment of inertia of the lesion shape and histograms. The FFBPN has a high architecture flexibility, which indicates it as a favorable tool to avoid the over-parameterization of the ANN and, equally, to discard those redundant input datasets that usually result in poor test performance. The FFBPN was tested on four public image datasets containing melanoma, dysplastic nevus and nevus images. Experimental results on multiple benchmark data sets demonstrate that asymmetry A2 is a meaningful feature for skin lesion classification, and FFBPN with 16 neurons in the hidden layer can model the data without compromising prediction accuracy.

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Section: Related Workmentioning

confidence: 99%

Towards Accurate Diagnosis of Skin Lesions Using Feedforward Back Propagation Neural Networks

et al. 2021

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“…Accuracy of melanoma detection for GLCM parameters is 79.3% and 78.2% using SVM and KNN, respectively, while for SURF features it is 87.3% and 85.2% using SVM and KNN, respectively. Kavitha et al [21] have used GLCM and color histograms as color features extracted from RGB, HSV and OPP color space. The classification is done using SVM with the best results found using combined GLCM and color histograms in RGB color space.…”

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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“…The identi ication of melanoma can be employed using various kinds of systems. One such method is called the global technique (Kavitha and Suruliandi, 2016). In this particular method, initially, the image is subjected to segmentation (simple adaptive threshold algorithm).…”

Section: Related Workmentioning

confidence: 99%

“…In the past, a mainly computer-aided pattern classi ication system for dermatoscopy images was used, which utilized a pattern classi ication sys- (Kavitha and Suruliandi, 2016). This method was seen to have accuracy and several parametric values for the skin tissue but had a setback of high computational complexity.…”

Section: Introductionmentioning

confidence: 99%

Synthesis and Characterization of New Ligand Derived from Benzimidazole and It's Complexes with Some Transition Metals

Jamel

Alobaidi

2018

Int. J. Res. Ph. Sci.

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In spite of the gargantuan number of patients affected by melanoma every year, its detection at an early stage is still a challenging task. This paper illustrates a method which involves the combination of the existing ABCD (Involving symmetry, border, color, and diameter detection) rule and grey level co-occurrence matrix (GLCM) along with Local Binary Pattern (LBP) for identi ication of malignant melanoma skin lesion with greater accuracy. Several steps, such as image acquisition technique, pre-processing (RGB to HSV) techniques and segmentation processes are undertaken for the skin feature selection criteria to successfully determine the skin lesion's characteristic properties for classi ication. Texture features such as contrast, entropy, energy and homogeneity of the affected region is obtained using LBP and GLCM for discriminatory purposes of the two cases (melanoma and non-melanoma). Finally, the back propagation neural network (BPN) is used as the classi ier to determine whether the dermoscopic image is benign or malignant.

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Texture and color feature extraction for classification of melanoma using SVM

Cited by 54 publications

References 11 publications

Towards Accurate Diagnosis of Skin Lesions Using Feedforward Back Propagation Neural Networks

Towards Accurate Diagnosis of Skin Lesions Using Feedforward Back Propagation Neural Networks

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

Synthesis and Characterization of New Ligand Derived from Benzimidazole and It's Complexes with Some Transition Metals

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