Automatic Detection of Melanoma Skin Cancer using Texture Analysis

Mabrouk, Mai S.; Sheha, Mariam A.; Sharawy, Amr

doi:10.5120/5817-8129

Cited by 84 publications

(24 citation statements)

References 16 publications

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“…There are many methods available for feature selection [148] which include principle component analysis [81] and search strategies like sequential forward selection (SFS) [149], sequential backward selection (SBS) [150], plus-l-take-away-r (PTA (l, r)), floating search methods [54, 151], sequential forward floating selection (SFFS), sequential backward floating selection (SBFS)) and Fisher score ranking [135]. All these algorithms use stepwise inclusions and exclusions of features into/from the subset of consideration, but they differ in their strategy of applying them.…”

Section: Computer-aided Diagnosis Systemmentioning

confidence: 99%

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

Masood

Al-Jumaily

2013

International Journal of Biomedical Imaging

283

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%

“…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%

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

Masood

Al-Jumaily

2013

International Journal of Biomedical Imaging

283

161

View full text Add to dashboard Cite

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“…Image processing has also been employed in skin disorders for the classification of their symptoms. The most important skin disease is the melanoma [14][15][16][17][18][19][20][21][22][23][24][25][26][27] but several others including mycosis, warts, papillomas, eczema, acne, vitiligo, etc., can also be recognized by images displaying skin lesions. The sources of these images are ordinary cameras and they are processed in Red-Green-Blue (RGB) color space.…”

Section: Introductionmentioning

confidence: 99%

A Review of Image Processing Techniques Common in Human and Plant Disease Diagnosis

Petrellis

2018

Symmetry

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Image processing has been extensively used in various (human, animal, plant) disease diagnosis approaches, assisting experts to select the right treatment. It has been applied to both images captured from cameras of visible light and from equipment that captures information in invisible wavelengths (magnetic/ultrasonic sensors, microscopes, etc.). In most of the referenced diagnosis applications, the image is enhanced by various filtering methods and segmentation follows isolating the regions of interest. Classification of the input image is performed at the final stage. The disease diagnosis approaches based on these steps and the common methods are described. The features extracted from a plant/skin disease diagnosis framework developed by the author are used here to demonstrate various techniques adopted in the literature. The various metrics along with the available experimental conditions and results presented in the referenced approaches are also discussed. The accuracy achieved in the diagnosis methods that are based on image processing is often higher than 90%. The motivation for this review is to highlight the most common and efficient methods that have been employed in various disease diagnosis approaches and suggest how they can be used in similar or different applications.

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“…Spectral technique is based on Fourier analysis of grey level image. Mabrouk [3] et al, in his paper proposes a automated method for melanoma diagnosis. The input images are a set of dermoscopic images.…”

Section: IIImentioning

confidence: 99%

Analysis on Skin Lesion Classification Systems and Dermoscopic Feature Analysis for Melanoma

Kumar¹

2018

IJRASET

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Cancer in the recent yeas is one of the significantly complex diseases for diagnostics. The early detection of a deadly form a cancer ie., skin cancer can be done by using image based computer aided diagnostic systems. These systems have a significant potential to screen and detect malignant melanoma. This paper discusses efficient methods using various algorithms for classification of skin cancer. This paper also reviews the different processing steps involved, the various features extracted and classifiers used to classify as melanoma or benign. We also review and examine the current practices, problems and suggest appropriate modeling methodologies.

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Automatic Detection of Melanoma Skin Cancer using Texture Analysis

Cited by 84 publications

References 16 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

A Review of Image Processing Techniques Common in Human and Plant Disease Diagnosis

Analysis on Skin Lesion Classification Systems and Dermoscopic Feature Analysis for Melanoma

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