The usage of deep learning algorithm in medical diagnostic of breast cancer

Diagnosis is a crucial step to identify the disease that experienced by the patient. Diagnosis includes information gathering, integration, and interpretation. However, diagnosis process is not an easy task. Diagnostic accuracy is depending on the experience and cognitive ability of diagnosticians. The new algorithm called deep learning that is developed by simulating the human visual mechanism has been implemented in medical diagnostics. One of the diseases that can be diagnosed by using deep learning algorithm is the breast cancer. Several studies showed that deep learning algorithm can be used for detecting and classifying lesions, detecting mitosis, and predicting specific gene status.  In this review article, 16 research journals were reviewed and discussed. The limitations of each algorithm are provided. All of the journals showed that deep learning algorithm has high diagnostics accuracy in assisting the professional diagnosticians to determine diagnosis outcome accordingly.

[1]  Catarina Eloy,et al.  Classification of breast cancer histology images using Convolutional Neural Networks , 2017, PloS one.

[2]  Fadhl M Alakwaa,et al.  Deep learning accurately predicts estrogen receptor status in breast cancer metabolomics data , 2017 .

[3]  Michel E. Vandenberghe,et al.  Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer , 2017, Scientific Reports.

[4]  É. Sebő,et al.  Short- and Long-Term (10-year) Results of an Organized, Population-Based Breast Cancer Screening Program: Comparative, Observational Study from Hungary , 2018, World Journal of Surgery.

[5]  Alex Zhavoronkov,et al.  Applications of Deep Learning in Biomedicine. , 2016, Molecular pharmaceutics.

[6]  Guang-Zhong Yang,et al.  Deep Learning for Health Informatics , 2017, IEEE Journal of Biomedical and Health Informatics.

[7]  Roland Memisevic Deep learning: Architectures, algorithms, applications , 2015, 2015 IEEE Hot Chips 27 Symposium (HCS).

[8]  Binjie Qin,et al.  Ultrasound Imaging Technologies for Breast Cancer Detection and Management: A Review. , 2018, Ultrasound in medicine & biology.

[9]  Thomas Theelen,et al.  Deep learning approach for the detection and quantification of intraretinal cystoid fluid in multivendor optical coherence tomography. , 2018, Biomedical optics express.

[10]  E. Finkelstein,et al.  Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes , 2017, JAMA.

[11]  Colin Raffel,et al.  Realistic Evaluation of Deep Semi-Supervised Learning Algorithms , 2018, NeurIPS.

[12]  Russ B. Altman,et al.  3D deep convolutional neural networks for amino acid environment similarity analysis , 2017, BMC Bioinformatics.

[13]  Nassir Navab,et al.  AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images , 2016, IEEE Trans. Medical Imaging.

[14]  Guo-Wei Wei,et al.  TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions , 2017, PLoS Comput. Biol..

[15]  U. Rajendra Acharya,et al.  Deep learning for healthcare applications based on physiological signals: A review , 2018, Comput. Methods Programs Biomed..

[16]  Jonathan Pevsner,et al.  Bioinformatics and functional genomics , 2003 .

[17]  M. A. Aswathy,et al.  Detection of breast cancer on digital histopathology images: Present status and future possibilities , 2017 .

[18]  Yuanjie Zheng,et al.  Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model , 2017, Scientific Reports.

[19]  E. Andrianantoandro,et al.  Synthetic biology: new engineering rules for an emerging discipline , 2006, Molecular systems biology.

[20]  Dayong Wang,et al.  Deep Learning for Identifying Metastatic Breast Cancer , 2016, ArXiv.

[21]  Xiong Li,et al.  Heterogeneity Analysis and Diagnosis of Complex Diseases Based on Deep Learning Method , 2018, Scientific Reports.

[22]  Weiwei Liu,et al.  An improved deep learning approach for detection of thyroid papillary cancer in ultrasound images , 2018, Scientific Reports.

[23]  Andrew H. Beck,et al.  Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer , 2017, JAMA.

[24]  Mitko Veta,et al.  Mitosis Counting in Breast Cancer: Object-Level Interobserver Agreement and Comparison to an Automatic Method , 2016, PloS one.

[25]  H. Ouellette-Kuntz,et al.  Care of adults with developmental disabilities: Effects of a continuing education course for primary care providers. , 2015, Canadian family physician Medecin de famille canadien.

[26]  Hyunjin Park,et al.  Prospects of deep learning for medical imaging , 2018, Precision and Future Medicine.

[27]  A. Redig,et al.  Breast cancer as a systemic disease: a view of metastasis , 2013, Journal of internal medicine.

[28]  D. Shen,et al.  Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans , 2016, Scientific Reports.

[29]  Geoffrey Zweig,et al.  Recent advances in deep learning for speech research at Microsoft , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[30]  Thomas Frauenfelder,et al.  Deep Learning in Mammography: Diagnostic Accuracy of a Multipurpose Image Analysis Software in the Detection of Breast Cancer , 2017, Investigative radiology.