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Deep learning has introduced several learning-based methods to recognizebreast tumours and presents high applicability in breast cancer diagnostics. Ithas presented itself as a practical installment in Computer-Aided Diagnostic(CAD) systems to further assist radiologists in diagnostics for differentmodalities. A deep learning network trained on images provided by hospitals orpublic databases can perform classification, detection, and segmentation oflesion types. Significant progress has been made in recognizing tumours on 2Dimages but recognizing 3D images remains a frontier so far. The interconnectionof deep learning networks between different fields of study help propelsdiscoveries for more efficient, accurate, and robust networks. In this reviewpaper, the following topics will be explored: (i) theory and application ofdeep learning, (ii) progress of 2D, 2.5D, and 3D CNN approaches in breasttumour recognition from a performance metric perspective, and (iii) challengesfaced in CNN approaches.

A survey on deep learning approaches for breast cancer diagnosis