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A Robust Deep Neural Network Based Breast Cancer Detection and Classification - 2020

A Robust Deep Neural Network Based Breast Cancer Detection And Classification

Research Area:  Machine Learning


The exponential upward push in breast cancer cases across the globe has alarmed academia-industries to obtain certain more effect and strong Breast cancer laptop Aided prognosis (BC-CAD) device for breast most cancers detection. Some of techniques have been evolved with focus on case centric segmentation, feature extraction and class of breast cancer Histopathological photos. However, rising complexity and accuracy regularly demands more sturdy answer. Recently, Convolutional Neural community (CNN) has emerged as one of the maximum efferent techniques for medical records evaluation and diverse picture classification issues. On this paper, a notably strong and green BC-CAD solution has been proposed. Our proposed gadget consists of pre-processing, more suitable adaptive learning based totally Gaussian aggregate model (GMM), connected element analysis based vicinity of interest localization, and AlexNet-DNN primarily based characteristic extraction. The precept factor analysis (PCA) and Linear Discriminant analysis (LDA) primarily based on characteristic selection that-s used as dimensional discount. One of the blessings of the proposed method is that not one of the current dimensional reduction algorithms hired with SVM to perform breast most cancers detection and class. The overall results acquired signify that the AlexNet-DNN based capabilities at completely connected layer; FC6 together with LDA dimensional discount and SVM-based totally classification outperforms other country-of-artwork techniques for breast cancer detection. The proposed method completed 96.20 for AlexNet-FC6 and 96.70 for AlexNet-FC7 in term of assessment measures.


Author(s) Name:  Romany F. Mansour

Journal name:  International Journal of Computational Intelligence and Applications

Conferrence name:  

Publisher name:  World Scientific

DOI:  10.1142/S1469026820500078

Volume Information:  Vol. 19, No. 01, 2050007 (2020)