Quantum inspired algorithm for microcalcification detection in mammograms

Rubio, Yoshio; Montiel, Oscar; Sepúlveda, Roberto

doi:10.1016/j.ins.2018.12.040

Cited by 6 publications

(2 citation statements)

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“…They achieved an AUC value of AZ = 0.918 by using 280 mammograms. Y. Rubio [31] used a novel methodology based on quantum signal processing and cellular automata to detect microcalcifications in mammograms. They achieved an AUC value of AZ = 0.8297 by using 121 mammograms.…”

Section: System Performance Evaluation Methodsmentioning

confidence: 99%

Breast Microcalcification Detection Algorithm Based on Contourlet and ASVM

et al. 2019

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Microcalcification is the most important landmark information for early breast cancer. At present, morphological artificial observation is the main method for clinical diagnosis of such diseases, but it is easy to cause misdiagnosis and missed diagnosis. The present study proposes an algorithm for detecting microcalcification on mammography for early breast cancer. Firstly, the contrast characteristics of mammograms are enhanced by Contourlet transformation and morphology (CTM). Secondly, split the ROI by the improved K-means algorithm. Thirdly, calculate grayscale feature, shape feature, and Histogram of Oriented Gradient (HOG) for the ROI region. The Adaptive support vector machine (ASVM) is used as a tool to classify the rough calcification point and the false calcification point. Under the guidance of a professional doctor, 280 normal images and 120 calcification images were selected for experimentation, of which 210 normal images and 90 images with calcification images were used for training classification. The remaining 100 are used to test the algorithm. It is found that the accuracy of the automatic classification results of the Adaptive support vector machine (ASVM) algorithm reaches 94%, and the experimental results are superior to similar algorithms. The algorithm overcomes various difficulties in microcalcification detection and has great clinical application value.

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Section: System Performance Evaluation Methodsmentioning

confidence: 99%

Breast Microcalcification Detection Algorithm Based on Contourlet and ASVM

et al. 2019

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“…Recently, much attention has been paid to the development of a robust tool for detecting abnormalities in digitized mammograms. A quantum-based approach 2 has been developed based on both quantum signal processing and cellular automata to detect microcalcifications in digitized mammograms. Fisher linear discriminant analysis 3 has been used by integrating features extracted based on neighbourhood structural similarity to distinguish mammographic masses into being benign or malignant.…”

Section: Introductionmentioning

confidence: 99%

Automated Classification of Malignant and Benign Breast Cancer Lesions Using Neural Networks on Digitized Mammograms

2019

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We propose a novel neural network approach for the classification of abnormal mammographic images into benign or malignant based on their texture representations. The proposed framework has the capability of mapping high dimensional feature space into a lowerdimension, in a supervised way. The main contribution of the proposed classifier is to introduce a new neuron structure for map representation and adopt a supervised learning technique for feature classification. This is achieved by making the weight updating procedure dependent on the class reliability of the neuron. We showed high accuracy (95.2%) for our proposed approach in the classification of abnormal real mammographic images when compared to other related methods.

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