Application of higher order GLCM features on mammograms

Gaike, Vrushali; Akhter, Nasreen; Kale, K. V.; Deshmukh, Prapti D.

doi:10.1109/icecct.2015.7226098

Cited by 6 publications

(2 citation statements)

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“…The classification accuracy is achieved up to 94% during training stage. Vrushali Gaike et al 31 attempted up to using 7th order in GLCM (Gray Level Co-occurrence Matrix) and observed the results by analyzing the effects of higher order features in recognition of malignancy in breast mammogram. The GLCM technique is used to compute sec-order statistical textural features.…”

Section: Survey On Various Classification Methodsmentioning

confidence: 99%

The Effect of Thermography on Breast Cancer Detection-A Survey

Pavithra¹,

Ks²,

Sekar³

et al. 2018

SRP

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Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. Cancer has generally an abnormal cell that grows and invade healthy cells in the body. Breast cancer occurs when malignant tumors develop in the breast. Early detection of the breast cancer can effective in increasing the rate of survival. There are several Medical image techniques which has been used for detecting breast cancer. The survey explored the needs for the thermography over other screening tools. Thermography detects and records temperature changes on the surface of the skin which is known as Digital Infrared Thermal Imaging (DITI). Thermography is known for its features like non-invasive, painless, cost effective and high chances of recovery, which has got ability to detect the breast cancer at near beginning. There is no such tool which provides accurate predictability unless biopsy. But the combination of tools with thermography may boost the sensitivity and specificity when compared with other mechanisms.

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Section: Survey On Various Classification Methodsmentioning

confidence: 99%

The Effect of Thermography on Breast Cancer Detection-A Survey

Pavithra¹,

Ks²,

Sekar³

et al. 2018

SRP

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“…After the face detection process, the input is subjected to feature extraction. In this, GLCM [20] and LVP based feature extraction are exploited to extract the facial image features. The final step is the classification, where the extracted facial features are classified using the NN classifier.…”

Section: Proposed Forgery Detection Modelmentioning

confidence: 99%

Face Image Forgery Detection by Weight Optimized Neural Network Model

2019

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This framework introduces a new automatic image forgery detection approach that involves four main stages like (i) Illumination map computation, (ii) Face detection, (iii) Feature extraction, and (iv) Classification. Initially, the processing of input image is exploited by means of illumination map estimation, which acquires two computation processes called Gray world estimates and Inverse-Intensity chromaticity. Subsequent to this, the Viola-Jones algorithm is employed for the face detection process, which is the second phase, in order to detect the face image clearly. Once after the detection process, the obtained facial image is subjected to feature extraction. For this, Grey Level Co-occurrence Matrix (GLCM) is exploited that extract the facial features from the image. After this, the classification process is carried out for the extracted facial features by employing the Neural Network (NN) classifier. On the whole, this paper mainly concerned over the optimization concept, in which the weight of the NN is optimally selected by using the renowned optimization algorithm named Whale Optimization Algorithm (WOA). To the end, the performance of the implemented model is compared over the other classical models like k-nearest neighbor (kNN), NN and Support Vector Machine (SVM) regarding certain measures like Accuracy, Sensitivity, and Specificity.

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