Diagnosis of breast cancer based on modern mammography using hybrid transfer learning

Breast cancer is a common cancer in women. Early detection of breast cancer in particular and cancer, in general, can considerably increase the survival rate of women, and it can be much more effective. This paper mainly focuses on the transfer learning process to detect breast cancer. Modified VGG (MVGG) is proposed and implemented on datasets of 2D and 3D images of mammograms. Experimental results showed that the proposed hybrid transfer learning model (a fusion of MVGG and ImageNet) provides an accuracy of 94.3%. On the other hand, only the proposed MVGG architecture provides an accuracy of 89.8%. So, it is precisely stated that the proposed hybrid pre-trained network outperforms other compared Convolutional Neural Networks. The proposed architecture can be considered as an effective tool for radiologists to decrease the false negative and false positive rates. Therefore, the efficiency of mammography analysis will be improved.

[1]  Amit Kumar Jaiswal,et al.  Multimodal medical image fusion algorithm in the era of big data , 2020, Neural Computing and Applications.

[2]  S. Shapiro,et al.  Ten- to fourteen-year effect of screening on breast cancer mortality. , 1982, Journal of the National Cancer Institute.

[3]  Massimo Melucci,et al.  Binary Classifier Inspired by Quantum Theory , 2019, AAAI.

[4]  Ali Tahir,et al.  Classification Of Breast Cancer Histology Images Using ALEXNET , 2018, ICIAR.

[5]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Auxiliadora Sarmiento,et al.  Automatic Breast Cancer Grading of Histological Images Based on Colour and Texture Descriptors , 2018, ICIAR.

[7]  Francisco Herrera,et al.  BreakHis based breast cancer automatic diagnosis using deep learning: Taxonomy, survey and insights , 2020, Neurocomputing.

[8]  Shahram Dehdashti,et al.  TermInformer: unsupervised term mining and analysis in biomedical literature , 2020, Neural computing & applications.

[9]  Yu-Dong Yao,et al.  Breast Cancer Detection Using Extreme Learning Machine Based on Feature Fusion With CNN Deep Features , 2019, IEEE Access.

[10]  Daniel L Rubin,et al.  A curated mammography data set for use in computer-aided detection and diagnosis research , 2017, Scientific Data.

[11]  Prajoy Podder,et al.  Data analytics for novel coronavirus disease , 2020, Informatics in Medicine Unlocked.

[12]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[13]  U. Rajendra Acharya,et al.  Automated invasive ductal carcinoma detection based using deep transfer learning with whole-slide images , 2020, Pattern Recognit. Lett..

[14]  Alexander Rakhlin,et al.  Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis , 2018, bioRxiv.

[15]  Xiaohui Xie,et al.  Deep Learning Framework for Multi-class Breast Cancer Histology Image Classification , 2018, ICIAR.

[16]  T. M. Kolb,et al.  Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them: an analysis of 27,825 patient evaluations. , 2002, Radiology.

[17]  Mohammad Atikur Rahman,et al.  Breast Cancer Prediction Applying Different Classification Algorithm with Comparative Analysis using WEKA , 2018, 2018 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT).

[18]  Shuyi Li,et al.  Attention Dense-U-Net for Automatic Breast Mass Segmentation in Digital Mammogram , 2019, IEEE Access.

[19]  Subrato Bharati,et al.  Brain Magnetic Resonance Imaging Compression Using Daubechies & Biorthogonal Wavelet with the fusion of STW and SPIHT , 2018, 2018 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE).

[20]  Scotty Kwok,et al.  Multiclass Classification of Breast Cancer in Whole-Slide Images , 2018, ICIAR.

[21]  D B Kopans,et al.  Breast cancer detection. , 1986, The Western journal of medicine.

[22]  Bo Chen,et al.  MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.

[23]  L. J. van der Kamp,et al.  False-positive findings in mammography screening induces short-term distress - breast cancer-specific concern prevails longer. , 2000, European journal of cancer.

[24]  Subrato Bharati,et al.  Disease Detection from Lung X-ray Images based on Hybrid Deep Learning , 2020, ArXiv.

[25]  Massimo Melucci,et al.  Towards a Quantum-Inspired Binary Classifier , 2019, IEEE Access.

[26]  Subrato Bharati,et al.  Comparative Performance Analysis of Different Classification Algorithm for the Purpose of Prediction of Lung Cancer , 2018, ISDA.

[27]  Hari Mohan Pandey,et al.  A Noble Double-Dictionary-Based ECG Compression Technique for IoTH , 2020, IEEE Internet of Things Journal.

[28]  Aditya Khamparia,et al.  Internet of health things-driven deep learning system for detection and classification of cervical cells using transfer learning , 2020, The Journal of Supercomputing.

[29]  S. Nathanson,et al.  Lymph Node Metastasis , 2017 .

[30]  X. Cui,et al.  Lymph Node Metastasis Prediction from Primary Breast Cancer US Images Using Deep Learning. , 2019, Radiology.

[31]  Richard H. Moore,et al.  Current Status of the Digital Database for Screening Mammography , 1998, Digital Mammography / IWDM.

[32]  Dang N. H. Thanh,et al.  A Review on CT and X-Ray Images Denoising Methods , 2019, Informatica.

[33]  Richard H. Moore,et al.  THE DIGITAL DATABASE FOR SCREENING MAMMOGRAPHY , 2007 .

[34]  Jafar A. Alzubi,et al.  Diversity Based Improved Bagging Algorithm , 2015 .

[35]  Prajoy Podder,et al.  Hybrid deep learning for detecting lung diseases from X-ray images , 2020, Informatics in Medicine Unlocked.

[36]  Ashish Khanna,et al.  Boosted neural network ensemble classification for lung cancer disease diagnosis , 2019, Appl. Soft Comput..

[37]  Massimo Melucci,et al.  Towards a Quantum-Inspired Framework for Binary Classification , 2018, CIKM.

[38]  Daniel L. Rubin,et al.  Probabilistic visual search for masses within mammography images using deep learning , 2015, 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[39]  Joel J. P. C. Rodrigues,et al.  Detection of subtype blood cells using deep learning , 2018, Cognitive Systems Research.

[40]  E. Halpern,et al.  Assessing radiologist performance using combined digital mammography and breast tomosynthesis compared with digital mammography alone: results of a multicenter, multireader trial. , 2013, Radiology.

[41]  Michael J. Kerin,et al.  Effects of Age on the Detection and Management of Breast Cancer , 2015, Cancers.

[42]  Vivek Kumar Singh,et al.  Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network , 2018, Expert Syst. Appl..

[43]  Mikhail Zymbler,et al.  Analyzing MRI scans to detect glioblastoma tumor using hybrid deep belief networks , 2020, Journal of Big Data.

[44]  Li Shen,et al.  End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Design , 2017, ArXiv.

[45]  Maryellen L. Giger,et al.  A deep learning methodology for improved breast cancer diagnosis using multiparametric MRI , 2020, Scientific Reports.

[46]  Nassir Navab,et al.  Structure-preserved color normalization for histological images , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[47]  Subrato Bharati,et al.  Lung cancer recognition and prediction according to random forest ensemble and RUSBoost algorithm using LIDC data , 2019, Int. J. Hybrid Intell. Syst..

[48]  Deepak Gupta,et al.  Optimal users based secure data transmission on the internet of healthcare things (IoHT) with lightweight block ciphers , 2019, Multimedia Tools and Applications.

[49]  Vivek Kumar,et al.  Prediction of Malignant & Benign Breast Cancer: A Data Mining Approach in Healthcare Applications , 2019, Advances in Data Science and Management.

[50]  Subrato Bharati,et al.  Artificial Neural Network Based Breast Cancer Screening: A Comprehensive Review , 2020, ArXiv.