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A Robust Deep Neural Network to Enhancement Features Extraction for Cancer Detection and Classification


Romany F. Mansour


Vol. 19  No. 4  pp. 123-139


The exponential rise in breast cancer cases across the globe has alarmed academia-industries to achieve certain more efficient and robust Breast Cancer Computer Aided Diagnosis (BC-CAD) system for breast cancer detection. A number of techniques have been developed with focus on case centric segmentation, feature extraction and classification of breast cancer Histopathological images. However, rising complexity and accuracy often demands more robust solution. Recently, Convolutional Neural Network (CNN) has emerged as one of the most efficient techniques for medical data analysis and various image classification problems. In this paper a highly robust and efficient BC-CAD solution has been proposed. Our proposed system incorporates pre-processing, enhanced adaptive learning based Gaussian Mixture Model (GMM), connected component analysis based region of interest localization, AlexNet-DNN based feature extraction. The Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA) based on feature selection which is used as dimensional reduction. One of the advantages of the proposed method is that none of the current dimensional reduction algorithms employed with SVM to perform breast cancer detection and classification. The overall results obtained signify that the AlexNet-DNN based features at fully connected layer FC6 in conjunction with LDA dimensional reduction and SVM based classification outperforms other state-of-art techniques for breast cancer detection. The proposed BC-CAD system has been performed over real world data BreakHis having significant diversity and complexity, and therefore we suggest it to be used for other real-world applications. The proposed method achieved 96.15 for AlexNet-FC6 and 96.20 for AlexNet-FC7 in term of evaluation measures.


Breast Cancer Detection Computer Aided Diagnosis Convolutional Neural Network AlexNet-DNN Linear Discriminant Analysis.