This is a preview and has not been published.

Breast Cancer MRI Classification Based on Fractional Entropy Image Enhancement and Deep Feature Extraction

Authors

  • Ali M. Hasan College of Medicine, Al-Nahrain University, Baghdad, Iraq. https://orcid.org/0000-0001-9151-6958
  • Asaad F. Qasim Ministry of Higher Education and Scientific Research, Studies, Planning and Follow-up Directorate, Baghdad, Iraq. https://orcid.org/0000-0003-2451-2871
  • Hamid A. Jalab Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia. https://orcid.org/0000-0002-4823-6851
  • Rabha W. Ibrahim Faculty of Mathematics and Statistics, Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam

DOI:

https://doi.org/10.21123/bsj.2022.6782

Keywords:

Breast MRI scans, Classification, CNN, Deep features, LSTM

Abstract

Disease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature extraction step to enhance and preserve the fine details of the breast MRI scans boundaries by using fractional integral entropy FIE algorithm, to reduce the effects of the intensity variations between MRI slices, and finally to separate the right and left breast regions by exploiting the symmetry information. The obtained features are classified using a long short-term memory (LSTM) neural network classifier. Subsequently, all extracted features significantly improves the performance of the LSTM network to precisely discriminate between pathological and healthy cases. The maximum achieved accuracy for classifying the collected dataset comprising 326 T2W-TSE images and 326 STIR images is 98.77%. The experimental results demonstrate that FIE enhancement method improve the performance of CNN in classifying breast MRI scans. The proposed model appears to be efficient and might represent a useful diagnostic tool in the evaluation of MRI breast scans.

Downloads

Download data is not yet available.

References

Breast cancer facts and figures 2019–2020. Am Cancer Soc: 1-44. Available from: https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/breast-cancer-facts-and-figures/breast-cancer-facts-and-figures-2019-2020.pdf.

Smith R, Brawley O, Wender R. Screening and Early Detection, Principles of Oncology. Am Cancer Soc. 2018: 110-135. Available from: https://doi.org/10.1002/9781119468868.ch11.

AL-Thaweni A, Yousif W, Hassan S. Detection of BRCA1and BRCA2 mutation for Breast Cancer in Sample of Iraqi Women above 40 Years. Baghdad Sci J. 2010; 7 (1): 394-400.

Marcon M, Ciritsis A, Rossi C, Becker A, Berger N, Wurnig M, et al. Diagnostic performance of machine learning applied to texture analysis-derived features for breast lesion characterisation at automated breast ultrasound: a pilot study. Eur Radiol Exp. 2019; 3(1): 44. Available from: https://doi.org/10.1186/s41747-019-0121-6.

Alnafea M. Detection and Diagnosis of Breast Diseases, in Breast Imaging. 2017, IntechOpen Book Series. Available from: http://doi.org/10.5772/intechopen.69898

Nahid A, Kong Y. Involvement of Machine Learning for Breast Cancer Image Classification: A Survey. Comput Math Methods Med. 2017; 2017:3781951. Available from: https://doi.org/10.1155/2017/3781951.

Hussain A, AL-Khafaji A, Ali A, Mohammed H. Study of Certain Biomarkers in Iraqi Female Patients with Breast Cancer. Baghdad Sci J. 2021; 18(4): 1140-1148. Available from: https://doi.org/10.21123/bsj.2021.18.4.1140.

Milosevic M, Jankovic D, Milenkovic A. Stojanov D, Early diagnosis and detection of breast cancer. Technol Health Care. 2018; 26(4): 729-759. Available from: https://content.iospress.com/articles/technology-and-health-care/thc181277

Dalmış M, Vreemann S, Kooi T, Mann R, Karssemeijer N, Gubern A. Fully automated detection of breast cancer in screening MRI using convolutional neural networks. J Med Imaging. 2018; 5(1): 014502. Available from: https://pubmed.ncbi.nlm.nih.gov/29340287/.

Lin C, Rogers C, Majidi S. Fat suppression techniques in breast magnetic resonance imaging: a critical comparison and state of the art. Rep Med. 2015; 8: 37-49. Available from: https://doi.org/10.2147/RMI.S46800

Kilic F, Ogul H, Bayraktutan U, Gumus H, Unal O, Kantarci M, et al. Diagnostic magnetic resonance imaging of the breast. Eurasian J Emerg Med. 2012; 44(2): 106. Available from: https://www.eajm.org//en/diagnostic-magnetic-resonance-imaging-of-the-breast-132589.

Jiménez-Gaona Y, Rodríguez-Álvarez M, Lakshminarayanan V. Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review. Appl Sci. 2020; 10(22): 8298. Available from: https://doi.org/10.3390/app10228298.

Vandeweyer E, Hertens D. Quantification of glands and fat in breast tissue: an experimental determination. Ann Anat. 2002; 184(2): 181-184. Available from: https://www.sciencedirect.com/science/article/abs/pii/S0940960202800164?via%3Dihub

Thomassin I, Trop I, Lalonde L, David J, Péloquin L, Chopier J. Tips and techniques in breast MRI. Diagn Interv Imaging. 2012; 93(11): 828-839. Available from: https://pubmed.ncbi.nlm.nih.gov/23084072/

Hilal S, Hasan H, Hasan A. Magnetic Resonance Imaging Breast Scan Classification based on Texture Features and Long Short-Term Memory Model. Neuro Quantology, 2021; 19(7): 41. Available from: https://www.proquest.com/openview/fc40762cbf3d70d60719e0201b402264/1.pdf?pq-origsite=gscholar&cbl=2035897.

Hasan A, Jalab H, Meziane F, Hasan K, Al-Ahmed A. Combining deep and handcrafted image features for MRI brain scan classification. IEEE Access. 2019; 7: 79959-79967. Available from: https://ieeexplore.ieee.org/document/8736208.

Hasan A, Jalab H, Ibrahim R, Meziane F, AL-Shamasneh A, Obaiys S. MRI brain classification using the quantum entropy LBP and deep-learning-based features. Entropy. 2020; 22(9): 1033. Available from: https://doi.org/10.3390/e22091033.

Jiang Y, Chen L, Zhang H, Xiao X. Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module. PloS one. 2019; 14(3): e0214587. Available from: https://doi.org/10.1371/journal.pone.0214587.

Dabeer S, Khan M, Islam S. Cancer diagnosis in histopathological image: CNN based approach. Inform Med Unlocked. 2019; 16: 100231. Available from: https://doi.org/10.1016/j.imu.2019.100231.

Xiang Z, Ting Z, Weiyan F, Cong L. Breast Cancer Diagnosis from Histopathological Image based on Deep Learning. Chin. Control Decis. Conf. 2019. IEEE. China. Available from: https://ieeexplore.ieee.org/document/8833431.

Khan S, Islam N, Zahoor J, Din I, Rodrigues J. A novel deep learning based framework for the detection and classification of breast cancer using transfer learning. Pattern Recognit. Lett. 2019; 125: 1-6. Available from: https://doi.org/10.1016/j.patrec.2019.03.022.

Yan R, Ren F, Wang Z, Zhang T, Liu Y, Rao X, et al. Breast cancer histopathological image classification using a hybrid deep neural network. Methods. 2020; 173: 52-60. Available from: https://doi.org/10.1016/j.ymeth.2019.06.014.

Lu W, Wang Z, He Y, Yu H, Xiong N, Jianguo W. Breast Cancer Detection Based on Merging Four Modes Mri Using Convolutional Neural Networks. Proc IEEE Int Conf Acoust Speech Signal Process. 2019. IEEE. United Kingdom. Available from: https://ieeexplore.ieee.org/document/8683149.

Yurttakal A, Erbay H, İkizceli T, Karaçavuş S. Detection of breast cancer via deep convolution neural networks using MRI images. Multimed. Tools Appl. 2020; 79: 15555–15573. Available from: https://link.springer.com/article/10.1007/s11042-019-7479-6.

Zhang Y, Chan S, Park V, Chang K, Mehta S, Kim M, et al. Automatic Detection and Segmentation of Breast Cancer on MRI Using Mask R-CNN Trained on Non–Fat-Sat Images and Tested on Fat-Sat Images. Acad Radiol. 2022;29 Suppl 1(Suppl 1): S135-S144. Available from: https://pubmed.ncbi.nlm.nih.gov/33317911/.

Zebari D, Ibrahim D, Zeebaree D, Haron H, Salih M. Damaševičius R., et al., Systematic Review of Computing Approaches for Breast Cancer Detection Based Computer Aided Diagnosis Using Mammogram Images. Appl Artif Intell. 2021: 1-47. Available from: https://doi.org/10.1080/08839514.2021.2001177.

Lahoura V, Singh H, Aggarwal A, Sharma B, Mohammed M, Damaševičius R, et al. Cloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machine. Diagnostics. 2021; 11(2): 241. Available from: https://doi.org/10.3390/diagnostics11020241.

Bloch B, Jain A, Jaffe C, Data From BREAST-DIAGNOSIS, T C I. Archive. 2020. Available from: http://doi.org/10.7937/K9/TCIA.2015.SDNRQXXR.

Jalab H, Ibrahim R. Fractional Alexander polynomials for image denoising. Signal Process. 2015; 107: 340-354. Available from: https://doi.org/10.1016/j.sigpro.2014.06.004.

Raghunandan K, Shivakumara P, Jalab H, Ibrahim R, Kumar G, Pal U, et al. Riesz fractional based model for enhancing license plate detection and recognition. IEEE Trans Circuits Syst Video Technol .2017; 28(9): 2276-2288. Available from: https://ieeexplore.ieee.org/document/7944571.

Roy S, Shivakumara P, Jalab H, Ibrahim R, Pal U, Lu T. Fractional poisson enhancement model for text detection and recognition in video frames. Pattern Recognit. 2016; 52: 433-447. Available from: https://doi.org/10.1016/j.patcog.2015.10.011.

Ibrahim R, Moghaddasi Z, Jalab H, Rafidah N. Fractional differential texture descriptors based on the machado entropy for image splicing detection. Entropy. 2015; 17(7): 4775-4785. Available from: https://doi.org/10.3390/e17074775.

Hasan A, AL-Jawad M, Jalab H, Shaiba H, Ibrahim R, AL-Shamasneh A. Classification of Covid-19 Coronavirus, Pneumonia and Healthy Lungs in CT Scans Using Q-Deformed Entropy and Deep Learning Features. Entropy. 2020; 22(5): 517. Available from: https://doi.org/10.3390/e22050517.

Al-Shamasneh A, Jalab H, Palaiahnakote S, Obaidellah U, Ibrahim R, El-Melegy M. A new local fractional entropy-based model for kidney MRI image enhancement. Entropy. 2018; 20(5): 344. Available from: https://doi.org/10.3390/e20050344.

Yang X, Baleanu D, Srivastava H. Local fractional integral transforms and their applications. 2015, AP. 1st Edition, Elsevier. Available from: https://doi.org/10.1016/B978-0-12-804002-7.09994-0.

Cicconet M, Hildebrand D, Elliott H. Finding mirror symmetry via registration. arXiv preprint arXiv: 2017; 1611.05971. Available from: https://arxiv.org/abs/1611.05971.

Xia L, Xi S, Yongxia Z, Xiuhui W, Tie-Qiang L. Classification of breast cancer histopathological images using interleaved DenseNet with SENet (IDSNet). PloS one. 2020; 15(5): e0232127. Available from: https://doi.org/10.1371/journal.pone.0232127.

Le X, Ho H, Lee G, Jung S. Application of long short-term memory (LSTM) neural network for flood forecasting. Water. 2019; 11(7): 1387. Available from: https://doi.org/10.3390/w11071387.

Jalab H, Hasan A, Magnetic Resonance Imaging Segmentation Techniques of Brain Tumors: A Review. Arch Neurosci. 2019; 6(Brain Mapping): e84920. Available from: https://brief.land/ans/articles/84920.html.

Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, et al. Imagenet large scale visual recognition challenge. Int J Comput Vis. 2015; 115(3): 211-252. Available from: https://link.springer.com/article/10.1007/s11263-015-0816-y.

Zhou B, Khosla A, Lapedriza A, Torralba A, Oliva A. An image database for deep scene understanding. arXiv preprint arXiv: 2016; 1610.02055. Available from: https://arxiv.org/abs/1610.02055.

Iandola F, Han S, Moskewicz M, Ashraf K, Dally W, Keutzer K. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size. arXiv preprint arXiv: 2016; 1602.07360. Available from: https://arxiv.org/abs/1602.07360.

Mai H, Mao Y, Dong T, Tan Y, Huang X, Wu S, et al. The utility of texture analysis based on breast magnetic resonance imaging in differentiating phyllodes tumors from fibroadenomas. Front Oncol. 2019; 9: 1021. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6803552/.

Downloads

Issue

Section

article