Towards Automated Breast Mass Classification using Deep Learning Framework Due to high variability in shape, structure and occurrence; the nonpalpable breast masses are often missed by the experienced radiologists. To aid them with more accurate identification, computeraided detection (CAD) systems are widely used. Most of the developed CAD systems use complex handcrafted features which introduce difficulties for further improvement in performance. Deep or highlevel features extracted using deep learning models already have proven its superiority over the low or middlelevel handcrafted features. In this paper, we propose an automated deep CAD system performing both the functions: mass detection and classification. Our proposed framework is composed of three cascaded structures: suspicious region identification, massnomass detection and mass classification. To detect the suspicious regions in a breast mammogram, we have used a deep hierarchical mass prediction network. Then we take a decision on whether the predicted lesions contain any abnormal masses using CNN highlevel features from the augmented intensity and wavelet features. Afterwards, the mass classification is carried out only for abnormal cases with the same CNN structure. The whole process of breast mass classification including the extraction of wavelet features is automated in this work. We have tested our proposed model on widely used DDSM and INbreast databases in which mass prediction network has achieved the sensitivity of 0.94 and 0.96 followed by a massnomass detection with the area under the curve (AUC) of 0.9976 and 0.9922 respectively on receiver operating characteristic (ROC) curve. Finally, the classification network has obtained an accuracy of 98.05% in DDSM and 98.14% in INbreast database which we believe is the best reported so far.

Deep Convolutional Neural Networks for Breast Cancer Detection Breast cancer is one of the main causes of cancer death worldwide. In the midst of the treatment of different disorders and diseases, one critical aspect in saving a patient is early detection. Correct detection and assessment of mammograms is hindered by humanerror and interobserver variations between pathologists. Existing convolutional neural network structures have shown promise in detection, but are hindered in their requirements for very large datasets to train on. The purpose of this paper is to explore a streamlined method classification of hematoxylin and eosin (Hu0026E) stained tissue cancer mammograms into noncarcinomas and carcinomas using a small training set. This is done by the creation of more sample sets through changing elements of the data such as shear ratio and rotation. We assumed a 4layer DCNN (deep convolutional neural network). We first train the DCNN with our augmented dataset, increasing dataset size by x200. We implement a highly accurate and reduced chance of overfitting gradient boosting algorithm. The overall classification accuracy of benign versus malignant was 88%.