Classification of melanoma and Clark nevus skin lesions based on medical image processing techniques

Nie, Dawei

doi:10.1109/iccrd.2011.5764239

Cited by 6 publications

(3 citation statements)

References 4 publications

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“…Atypical skin structures result in colour coordinates that deviate from the normal surface patch. Some researchers [ 61 , 117 , 118 , 134 , 135 ] used GLCM-based texture features [ 136 – 138 ] like dissimilarity, contrast, energy, maximum probability, correlation, entropy, and so forth.…”

Section: Computer-aided Diagnosis Systemmentioning

confidence: 99%

“…Train to test ratio is another important factor effecting the classification result. It has been observed [ 134 ] that as the training-set size increases, the results improve. The effect of train/test ratios on classification accuracy is studied in [ 196 ] and the best classification results were reached with 70/30 train to test ratio.…”

Section: Computer-aided Diagnosis Systemmentioning

confidence: 99%

See 1 more Smart Citation

Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms

Masood

Al-Jumaily

2013

International Journal of Biomedical Imaging

280

161

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Image-based computer aided diagnosis systems have significant potential for screening and early detection of malignant melanoma. We review the state of the art in these systems and examine current practices, problems, and prospects of image acquisition, pre-processing, segmentation, feature extraction and selection, and classification of dermoscopic images. This paper reports statistics and results from the most important implementations reported to date. We compared the performance of several classifiers specifically developed for skin lesion diagnosis and discussed the corresponding findings. Whenever available, indication of various conditions that affect the technique's performance is reported. We suggest a framework for comparative assessment of skin cancer diagnostic models and review the results based on these models. The deficiencies in some of the existing studies are highlighted and suggestions for future research are provided.

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Section: Computer-aided Diagnosis Systemmentioning

confidence: 99%

Section: Computer-aided Diagnosis Systemmentioning

confidence: 99%

Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms

Masood

Al-Jumaily

2013

International Journal of Biomedical Imaging

280

161

View full text Add to dashboard Cite

show abstract

“…First, variations in lighting and slide processing make it difficult to characterize melanoma. The use of relative color [27,30,31], where the average background skin color is subtracted from each lesion pixel, has been used to help compensate for color variability in the imaging process. The use of the relative color technique tries to remove variability of color due to different skin types as well as to lighting and image processing techniques [30].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Color Clustering for Melanoma Diagnosis in Dermoscopy Images

et al. 2017

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A fuzzy logic-based color histogram analysis technique is presented for discriminating benign skin lesions from malignant melanomas in dermoscopy images. The approach extends previous research for utilizing a fuzzy set for skin lesion color for a specified class of skin lesions, using alpha-cut and support set cardinality for quantifying a fuzzy ratio skin lesion color feature. Skin lesion discrimination results are reported for the fuzzy clustering ratio over different regions of the lesion over a data set of 517 dermoscopy images consisting of 175 invasive melanomas and 342 benign lesions. Experimental results show that the fuzzy clustering ratio applied over an eight-connected neighborhood on the outer 25% of the skin lesion with an alpha-cut of 0.08 can recognize 92.6% of melanomas with approximately 13.5% false positive lesions. These results show the critical importance of colors in the lesion periphery. Our fuzzy logic-based description of lesion colors offers relevance to clinical descriptions of malignant melanoma.

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A review on smartphone skin cancer diagnosis apps in evaluation and benchmarking: coherent taxonomy, open issues and recommendation pathway solution

Zaidan

Albahri

et al. 2018

Health Technol.

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Classification of melanoma and Clark nevus skin lesions based on medical image processing techniques

Cited by 6 publications

References 4 publications

Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms

Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms

Fuzzy Color Clustering for Melanoma Diagnosis in Dermoscopy Images

A review on smartphone skin cancer diagnosis apps in evaluation and benchmarking: coherent taxonomy, open issues and recommendation pathway solution

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