Melanoma Detection Using HSV with SVM Classifier and De-duplication Technique to Increase Efficiency

Patil, Mayura; Dongre, Nilima

doi:10.1007/978-981-15-6648-6_9

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…It is important to consider that SVM is not so popular with large data sets as it requires a significant amount of training time; however, in our research, this was not the case [ 56 ]. In comparison to the latest research work on SVM classification for melanoma (85.19% [ 58 ], 92.1% [ 59 ], 96% [ 60 ], 90% [ 61 ], 97.32% [ 62 ]), we achieved 98.9% accuracy with the proposed technique.…”

Section: Resultsmentioning

confidence: 74%

Diagnostics of Melanocytic Skin Tumours by a Combination of Ultrasonic, Dermatoscopic and Spectrophotometric Image Parameters

et al. 2020

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Dermatoscopy, high-frequency ultrasonography (HFUS) and spectrophotometry are promising quantitative imaging techniques for the investigation and diagnostics of cutaneous melanocytic tumors. In this paper, we propose the hybrid technique and automatic prognostic models by combining the quantitative image parameters of ultrasonic B-scan images, dermatoscopic and spectrophotometric images (melanin, blood and collagen) to increase accuracy in the diagnostics of cutaneous melanoma. The extracted sets of various quantitative parameters and features of dermatoscopic, ultrasonic and spectrometric images were used to develop the four different classification models: logistic regression (LR), linear discriminant analysis (LDA), support vector machine (SVM) and Naive Bayes. The results were compared to the combination of only two techniques out of three. The reliable differentiation between melanocytic naevus and melanoma were achieved by the proposed technique. The accuracy of more than 90% was estimated in the case of LR, LDA and SVM by the proposed method.

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Section: Resultsmentioning

confidence: 74%

Diagnostics of Melanocytic Skin Tumours by a Combination of Ultrasonic, Dermatoscopic and Spectrophotometric Image Parameters

et al. 2020

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“…Three classifiers are used in (Patil and Dongre, 2020) to detect melanoma images from a collection of PH2 dermoscopic images. For training and testing purposes, KNN, Naive Bayes, and SVM classifiers are used.…”

Section: Related Workmentioning

confidence: 99%

Multi-features extraction based on deep learning for skin lesion classification

2022

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For various forms of skin lesion, many different feature extraction methods have been investigated so far. Indeed, feature extraction is a crucial step in machine learning processes. In general, we can distinct handcrafted and deep learning features. In this paper, we investigate the efficiency of using 17 commonly pre-trained convolutional neural networks (CNN) architectures as feature extractors and of 24 machine learning classifiers to evaluate the classification of skin lesions from two different datasets: ISIC 2019 and PH2. In this research, we find out that a DenseNet201 combined with Fine KNN or Cubic SVM achieved the best results in accuracy (92.34% and 91.71% ) for the ISIC 2019 dataset. The results also show that the suggested method outperforms others approaches with an accuracy of 99% on the PH2 dataset.

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“…Employing Takens-Maine theorem, the RPS of a TS can be defined as follows: (13) in which the embedding dimension and time delay are presented as m and  [34,41].…”

Section: Ii) Permutation Entropy (Permen)mentioning

confidence: 99%

A Machine Learning Method Based on the Combination of Nonlinear and Texture Features to Diagnose Malignant Melanoma From Dermoscopic Images

Ghahfarrokhi

Khodadadi

2021

Preprint

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Skin cancer affects people of all skin tones, including those with darker complexions. Melanomas are known as the malignant tumors of skin cancer, resulting in an adverse prognosis, responsible for most deaths relating to skin cancer. Early diagnosis and treatment of skin cancer from dermoscopic images can significantly reduce mortality and save lives. Although several Computer-Aided Diagnosis (CAD) systems with satisfactory performance have been introduced in the literature for skin cancer detection, the high false detection rate has made it inevitable to have an expert physician for more examination. In this paper, a CAD system based on machine learning algorithms is provided to classify various skin cancer types. The proposed method uses the Online Region-based Active Contour Model (ORACM) to extract the Region Of Interest (ROI) of skin lesions. This model uses a new binary level set equation and regularization operation such as morphological opening and closing.Additionally, various combinations of different textures and nonlinear features are extracted for the ROI to show the multiple aspects of skin lesions. Several metaheuristic optimization algorithms are used to remove redundant or irrelevant features and reduce the feature space dimension. These are applied to the combination of the extracted features in which, Non-dominated Sorting Genetic Algorithm (NSGA II) as a multi-objective optimization algorithm has the best performance. Furthermore, various machine learning algorithms include K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Fitting neural network (Fit net), Feed-Forward neural network (FF net), and Pattern recognition network (Pat net) are employed for the classification. Accordingly, the best-obtained precision of 99.24% based on five-fold cross-validation is attained by the selected features of texture and nonlinear indices through NSGA II, applying the pattern net classifier. Also, the comparison between this paper's experimental results and other similar works with the same dataset demonstrates the proposed method's efficiency.

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Melanoma Detection Using HSV with SVM Classifier and De-duplication Technique to Increase Efficiency

Cited by 3 publications

References 15 publications

Diagnostics of Melanocytic Skin Tumours by a Combination of Ultrasonic, Dermatoscopic and Spectrophotometric Image Parameters

Diagnostics of Melanocytic Skin Tumours by a Combination of Ultrasonic, Dermatoscopic and Spectrophotometric Image Parameters

Multi-features extraction based on deep learning for skin lesion classification

A Machine Learning Method Based on the Combination of Nonlinear and Texture Features to Diagnose Malignant Melanoma From Dermoscopic Images

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