A comparison of detrend fluctuation analysis, Gaussian mixture model and artificial neural network performance in the detection of microcalcification from digital mammograms Online publication date: Mon, 13-Sep-2021
by Sannasi Chakravarthy S R; Harikumar Rajaguru
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 37, No. 1, 2021
Abstract: This paper presents a computer aided approach that classifies the type of cancer (benign or malignant) and its associated risk from the digital mammogram images. Twelve statistical features are extracted through five different wavelets such as Daubechies, Haar, biorthogonal splines, symlets and DMeyer with the decomposition levels of 4 and 6. The Mammogram Image Analysis Society (MIAS) database is utilised in this paper. The micro-calcification in the digital mammogram images is detected by detrend fluctuation analysis (DFA), Gaussian mixture model (GMM) and artificial neural network (ANN). The classifiers' performances are analysed and compared based on the benchmark parameters like sensitivity, selectivity, precision and accuracy. GMM classifier outperforms the DFA and ANN classifiers in terms of performance metrics.
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