%I Institute of Electrical and Electronics Engineers Inc.
%A N.A.N. Azlan
%A I. Elamvazuthi
%A T.B. Tang
%A C.-K. Lu
%T Breast Cancer Detection by Hybrid Techniques based on Deep Learning Networks
%R 10.1109/ICIAS49414.2021.9642670
%D 2021
%J International Conference on Intelligent and Advanced Systems: Enhance the Present for a Sustainable Future, ICIAS 2021
%L scholars15472
%O cited By 0; Conference of 8th International Conference on Intelligent and Advanced Systems, ICIAS 2021 ; Conference Date: 13 July 2021 Through 15 July 2021; Conference Code:175661
%X One of the cancers that gives high fatality rates in human life is breast cancer. The current method used to detect breast cancer needs radiologists, which makes it costly and time-consuming. A possible solution is to detect it early, which can be done by computer-aided diagnosis technologies. An end-to-end system that could automatically detect breast cancer is described in this paper. From the mammographic images, it was first undergone the pre-processing stages for noise elimination. The law's mask was then applied to the preprocessed image to filter out the secondary features further. The filtered image was segmented by the active contour to obtain the breast region before being fed into a deep convolutional neural network for feature extraction. Principle Component Analysis (PCA) technique was then applied to select the necessary features as input to the Support Vector Machine (SVM) for determining the class of cells (normal or abnormal). Lastly, k-fold cross-validation techniques were executed to validate the results and obtained the average reading for both training and testing datasets. The proposed system was tested on the Digital Database for Screening Mammography (DDSM) and Mammographic Image Analysis Society (MIAS) dataset, and attained 97.50, 96.67, 98.33, and 0.99 for accuracy, sensitivity, specificity, and area under curve, respectively. © 2021 IEEE.
%K Computer aided diagnosis; Convolution; Convolutional neural networks; Deep neural networks; Diseases; Feature extraction; Mammography; Principal component analysis; X ray screens, 'current; Active contours; Breast Cancer; Breast cancer detection; Diagnosis technology; Fatality rates; Human lives; Hybrid techniques; Learning network; Support vectors machine, Support vector machines