GLCM and Multi Class Support vector machine based automated skin cancer classification

Maurya, Ritesh; Singh, Surya Kant; Maurya, Ashish Kumar; Kumar, Ajeet

doi:10.1109/indiacom.2014.6828177

Cited by 44 publications

(15 citation statements)

References 12 publications

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“…Graycomatrix utility is made use of to generate GLCM by estimating how frequently a pixel amid intensity value i come about in an explicit spatial connection to pixel amid value j. Every factor (i, j) in the resultant GLCM is basically the summation of the amount of times the pixel through value i transpired in a particular spatial connection to a pixel amid value j in the input figure [18]. GLCM extricates second order dermographic textural features named as: Autocorrelation, Cluster Entropy prominence, Cluster shade, Contrast, Correlation, Difference entropy, Difference variance, Dissimilarity, Energy, Homogeneity, Information measure of correlation 1, Information measure of correlation 2, Inverse difference, Maximum probability, Sum average, Sum entropy, Sum of squares variance, Sum variance.…”

Section: Glcm Featuresmentioning

confidence: 99%

“…Few analysts offered to accomplish segmentation [17] and on the basis of this, classify the melanoma as benign and malignant. The latest research reveals that the smart phone application can be much beneficial for the diagnosis of skin cancer [18]..From literature, it may be noticed that an improved algorithm, in order to segment the skin abrasion is a prerequisite task. Also the literature reveals the use of statistical, structural and textural features.…”

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confidence: 99%

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Improved Technique to Diagnose Skin Cancer using Advanced Image Processing and Machine Learning Techniques

2019

IJITEE

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Skin cancer is being classified among the mortal and exaggerating forms of cancer since decade. Notwithstanding, the early diagnosis of skin cancer is very important and it is an extravagant procedure. The presence of human skin is tough to examine and to model. This is because of its complex surface. The difficulty of the irregular edge, tone, appearance of thick hair and other alleviating features generate the skin tough to be analyzed. Human skin has some non-identical sorts of textures that diseased skin can characterize between the textures of the healthy one. In consequence, considerable achievements have not been brought into the evolution of diagnosis approach for skin cancer. On the other hand, skin cancer diagnosis in dermoscopic portrayals is an exceptionally troublesome chore. Notwithstanding, the early diagnosis of skin cancer is very important and it is an extravagant procedure. In consequence, considerable achievements have not been brought into the evolution of diagnosis approach for skin cancer. For precise diagnosis and categorization of skin cancer, particular features are recommended that one may categorize benign and malignant illustration. A midst analysts, governing the efficacious mechanism of skin cancer diagnosis is an all-important matter of contention. Ascertaining the more proficient methods of diagnosis to minimize the amount of errors is a crucial subject among analysts. Image processing is used to recognize the affected area by disease, its form and color etc. The reason behind it is to minimize the percentage of miscalculations. Computer vision can bring about influential contribution in skin cancer detection. Computer Aided Diagnosis provides support in the premature diagnosis of skin cancer. Programmed skin abrasion segregation is a stimulating chore because of the low contrast between abrasion and encompassing skin and asymmetrical periphery. In this paper we present an amalgamation of statistical features, GLCM features and GLRLM features. The recommended methodology is enacted on 100 images from PH2 database. In this work 2 segmentation tactics namely Otsu’s thresholding and Region growing have been explored. The performance of two systems is measured in terms of sensitivity, specificity, accuracy, precision, recall and f-measure

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Section: Glcm Featuresmentioning

confidence: 99%

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confidence: 99%

Improved Technique to Diagnose Skin Cancer using Advanced Image Processing and Machine Learning Techniques

2019

IJITEE

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“…The other leather classification uses hand-crafted features which are color moments and some statistical measurements from Gray Level Co-Occurrence Matrix (GLCM). GLCM is well known texture features extraction method and has been used in many texture based classification in wide range of applications [15]- [18]. The features are also classified using SVM.…”

Section: Related Workmentioning

confidence: 99%

Pre-Trained Convolutional Neural Network for Classification of Tanning Leather Image

Winiarti¹,

Prahara²,

Murinto³

et al. 2018

ijacsa

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Abstract-Leather craft products, such as belt, gloves, shoes, bag, and wallet are mainly originated from cow, crocodile, lizard, goat, sheep, buffalo, and stingray skin. Before the skins are used as leather craft materials, they go through a tanning process. With the rapid development of leather craft industry, an automation system for leather tanning factories is important to achieve large scale production in order to meet the demand of leather craft materials. The challenges in automatic leather grading system based on type and quality of leather are the skin color and texture after tanning process will have a large variety within the same skin category and have high similarity with the other skin categories. Furthermore, skin from different part of animal body may have different color and texture. Therefore, a leather classification method on tanning leather image is proposed. The method uses pre-trained deep convolution neural network (CNN) to extract rich features from tanning leather image and Support Vector Machine (SVM) to classify the features into several types of leather. Performance evaluation shows that the proposed method can classify various types of leather with good accuracy and superior to other state-of-the-art leather classification method in terms of accuracy and computational time.

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“…Segmentation of image deals with the process of partitioning a digital image into multiple segments (sets of different pixels) the goal of this process is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. [16]. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.)…”

Section: Image Segmentationmentioning

confidence: 99%

Neural Network Based Detection of Melanoma Skin Cancer

2016

IJSR

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Skin cancer is the deadliest form of cancers in humans. It is found in various types such as Melanoma, Basal and Squamous cell Carcinoma among which Melanoma is the most unpredictable. Detection of melanoma skin cancer in the earlier stage is very critical. In this paper, we explain the method for the detection of Melanoma Skin Cancer using Segmentation. The input to the system is the Dermoscopic Image and then apply and then by applying novel image processing techniques. The different stages of detection involves-collection of Dermoscopic Images, filtering the images by using Dull Razor filtering for removing hairs and air bubbles in the image, converting to gray scale, contrast enhancement, noise filtering, segmenting the images using Maximum Entropy Threshold. Feature Extraction technique used is Gray Level Co-occurrence Matrix (GLCM).It is a powerful tool for image feature extraction by mapping the gray level co-occurrence probabilities based on spatial relations of pixels in different angular directions. They are mean, standard deviation, Skewness, Kurtosis, Contrast, Energy and Homogeneity. Mean or expected value provides a measure of distribution. The sensitivity and specificity for diagnosis of melanoma achieved by neural network analysis of Raman spectra were 85% and 99%, respectively. We propose that neural network analysis of near-infrared Fourier transform Raman spectra could provide a novel method for rapid, automated skin cancer diagnosis on unstained skin samples.

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GLCM and Multi Class Support vector machine based automated skin cancer classification

Cited by 44 publications

References 12 publications

Improved Technique to Diagnose Skin Cancer using Advanced Image Processing and Machine Learning Techniques

Improved Technique to Diagnose Skin Cancer using Advanced Image Processing and Machine Learning Techniques

Pre-Trained Convolutional Neural Network for Classification of Tanning Leather Image

Neural Network Based Detection of Melanoma Skin Cancer

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