Detection of Microcalcification Using the Wavelet Based Adaptive Sigmoid Function and Neural Network

Sanjeev Kumar and Mahesh Chandra
Volume: 13, No: 4, Page: 703 ~ 715, Year: 2017
10.3745/JIPS.01.0007
Keywords: Cascade-Forward Back Propagation Technique, Computer-Aided Diagnosis (CAD), Contrast Limited Adaptive Histogram Equalization (CLAHE), Gray-Level Co-Occurrence Matrix (GLCM), Mammographic Image Analysis Society (MIAS) Database, Modified Sigmoid Function
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Abstract
Mammogram images are sensitive in nature and even a minor change in the environment affects the quality of the images. Due to the lack of expert radiologists, it is difficult to interpret the mammogram images. In this paper an algorithm is proposed for a computer-aided diagnosis system, which is based on the wavelet based adaptive sigmoid function. The cascade feed-forward back propagation technique has been used for training and testing purposes. Due to the poor contrast in digital mammogram images it is difficult to process the images directly. Thus, the images were first processed using the wavelet based adaptive sigmoid function and then the suspicious regions were selected to extract the features. A combination of texture features and gray- level co-occurrence matrix features were extracted and used for training and testing purposes. The system was trained with 150 images, while a total 100 mammogram images were used for testing. A classification accuracy of more than 95% was obtained with our proposed method.

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Cite this article
IEEE Style
Sanjeev Kumar and Mahesh Chandra, "Detection of Microcalcification Using the Wavelet Based Adaptive Sigmoid Function and Neural Network," Journal of Information Processing Systems, vol. 13, no. 4, pp. 703~715, 2017. DOI: 10.3745/JIPS.01.0007.

ACM Style
Sanjeev Kumar and Mahesh Chandra, "Detection of Microcalcification Using the Wavelet Based Adaptive Sigmoid Function and Neural Network," Journal of Information Processing Systems, 13, 4, (2017), 703~715. DOI: 10.3745/JIPS.01.0007.