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ME-CCNN: Multi-encoded images and a cascade convolutional neural network for breast tumor segmentation and recognition

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Abstract

Breast tumor segmentation and recognition from mammograms play a key role in healthcare and treatment services. As different tumors in mammography have dissimilar densities, shapes, sizes, and edges, the interpretation of mammograms can be time-consuming and prone to interpretation variability even for a highly trained radiologist or expert. In this study, several encoding approaches are first proposed to achieve an effective breast cancer recognition system as well as create new images from the input image. Each encoded image represents some unique features that are crucial for detecting the target texture properly. Subsequently, pectoral muscle is eliminated using obtained features from these encoded images. Moreover, 11 distinct images are then applied to a shallow and efficient cascade Convolutional Neural Network (CNN) for classifying each pixel inside the image. This network accepts 11 local patches as the input from 11 obtained encoded images. Next, all extracted features are concatenated to a vertical vector to apply to the fully connected layers. Using different representations of the input mammogram images, the suggested model is able to analyze the input texture more effectively without using a deep CNN model. Finally, comprehensive experiments are then conducted on two public datasets which then demonstrate that the proposed framework successfully is able to gain competitive outcomes compared to a number of baselines.

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References

  • Abdar M, Pourpanah F, Hussain S, Rezazadegan D, Liu L, Ghavamzadeh M, Fieguth P, Cao X, Khosravi A, Acharya UR, Makarenkov V, Nahavandi S (2021) A review of uncertainty quantification in deep learning: Techniques, applications and challenges. Information Fusion 76:243–297

    Google Scholar 

  • Abdelhafiz D, Yang C, Ammar R, Nabavi S (2019) Deep convolutional neural networks for mammography: advances, challenges and applications. BMC Bioinform 20(11):1–20

    Google Scholar 

  • Aiman A, Shen Y, Bendechache M, Inayat I, Kumar T (2021) AUDD: audio urdu digits dataset for automatic audio urdu digit recognition. Appl Sci 11(19):8842

    Google Scholar 

  • Al-antari MA, Al-masni MA, Choi MT, Han SM, Kim TS (2018) A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification. Int J Med Informatics 117:44–54

    Google Scholar 

  • Aleem S, Kumar T, Little S, Bendechache M, Brennan R, McGuinness K (2021) Random data augmentation based enhancement: ageneralized enhancement approach for medical datasets. arXiv. https://doi.org/10.48550/arXiv.2210.00824

    Article  Google Scholar 

  • Ali, E., Caputo, A., Lawless, S. & Conlan, O. 2021. Where Should I Go? A Deep Learning Approach to Personalize Type-Based Facet Ranking for POI Suggestion. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 13080 LNCS: 207–215.

  • Anari S, Tataei Sarshar N, Mahjoori N, Dorosti S, Rezaie A (2022) Review of deep learning approaches for thyroid cancer diagnosis. Math Probl Eng 2022:1–8

    Google Scholar 

  • Asgari Taghanaki S, Abhishek K, Cohen JP, Cohen-Adad J, Hamarneh G (2021) Deep semantic segmentation of natural and medical images: a review. Artif Intell Rev 54(1):137–178

    Google Scholar 

  • Azary H, Abdoos M (2020) A Semi-supervised method for tumor segmentation in mammogram images. J Med Signals Sens 10(1):12–18

    Google Scholar 

  • Baghban A, Bahadori M, Lemraski AS, Bahadori A (2018) Prediction of solubility of ammonia in liquid electrolytes using Least Square Support Vector Machines. Ain Shams Eng J 9(4):1303–1312

    Google Scholar 

  • Baseri Saadi S, Moreno-Rabié C, van den Wyngaert T, Jacobs R (2022a) Convolutional neural network for automated classification of osteonecrosis and related mandibular trabecular patterns. Bone Rep 17:101632

    Google Scholar 

  • Baseri Saadi S, Tataei Sarshar N, Sadeghi S, Ranjbarzadeh R, Kooshki Forooshani M, Bendechache M (2022b) Investigation of Effectiveness of Shuffled Frog-Leaping Optimizer in Training a Convolution Neural Network. J Healthc Eng 2022:1–11

    Google Scholar 

  • Chakraborty A, Ganguly D, Caputo A, Jones GJF (2020) Kernel density estimation based factored relevance model for multi-contextual point-of-interest recommendation. Inform Retr J 25(1):44–90

    Google Scholar 

  • Chandio, A., Gong, G.†, Kumar, T., Ullah, I., Ranjbarzadeh, R., Roy, A.M., Hussain, A. & Shen, Y. 2022. Precise Single-stage Detector.

  • Chen G, Li Q, Shi F, Rekik I, Pan Z (2020) RFDCR: Automated brain lesion segmentation using cascaded random forests with dense conditional random fields. Neuroimage 211:116620

    Google Scholar 

  • Chougrad H, Zouaki H, Alheyane O (2018) Deep Convolutional Neural Networks for breast cancer screening. Comput Methods Programs Biomed 157:19–30

    Google Scholar 

  • Das P, Das A (2019) A fast and automated segmentation method for detection of masses using folded kernel based fuzzy c-means clustering algorithm. Appl Soft Comput J 85:105775

    Google Scholar 

  • Dolz J, Desrosiers C, Ben Ayed I (2018) 3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study. Neuroimage 170:456–470

    Google Scholar 

  • Dubey SR (2019) Local directional relation pattern for unconstrained and robust face retrieval. Multimed Tools Appl 78(19):28063–28088

    Google Scholar 

  • El Idrissi El Kaitouni S, Abbad A, Tairi H (2018) A breast tumors segmentation and elimination of pectoral muscle based on hidden markov and region growing. Multi Tools Appl 77(23):31347–31362

    Google Scholar 

  • Geng D, Innes J, Wu W, Wang G (2021) Impacts of COVID-19 pandemic on urban park visitation: a global analysis. J Forest Res 32(2):553–567

    Google Scholar 

  • George M, Chen Z, Zwiggelaar R (2019) Multiscale connected chain topological modelling for microcalcification classification. Comput Biol Med 114:103422

    Google Scholar 

  • Ghoushchi SJ, Ranjbarzadeh R, Najafabadi SA, Osgooei E, Tirkolaee EB (2021) An extended approach to the diagnosis of tumour location in breast cancer using deep learning. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-021-03613-y

    Article  Google Scholar 

  • Hai J, Qiao K, Chen J, Tan H, Xu J, Zeng L, Shi D, Yan B (2019) Fully convolutional densenet with multiscale context for automated breast tumor segmentation. J Healthcare Eng. https://doi.org/10.1155/2019/8415485

    Article  Google Scholar 

  • Hamzenejad A, Ghoushchi SJ, Baradaran V, Mardani A (2020) A robust algorithm for classification and diagnosis of brain disease using local linear approximation and generalized Autoregressive Conditional Heteroscedasticity model. Mathematics 8(8):1268

    Google Scholar 

  • Haralick RM, Dinstein I, Shanmugam K (1973) Textural Features for Image Classification. IEEE Trans Syst, Man Cybern 3(6):610–621

    Google Scholar 

  • Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, Pal C, Jodoin PM, Larochelle H (2017) Brain tumor segmentation with deep neural networks. Med Image Anal 35:18–31

    Google Scholar 

  • Ho DJ, Yarlagadda DVK, D’Alfonso TM, Hanna MG, Grabenstetter A, Ntiamoah P, Brogi E, Tan LK, Fuchs TJ (2021) Deep Multi-Magnification Networks for multi-class breast cancer image segmentation. Comput Med Imaging Graph 88:101866

    Google Scholar 

  • Hu R, Zhu X, Zhu Y, Gan J (2020) Robust SVM with adaptive graph learning. World Wide Web 23(3):1945–1968

    Google Scholar 

  • Husain F, Dellen B, Torras C (2017) Scene Understanding Using Deep Learning Handbook of Neural Computation. Elsevier, Amsterdam, pp 373–382

    Google Scholar 

  • Kirsch RA (1971) Computer determination of the constituent structure of biological images. Comput Biomed Res 4(3):315–328

    Google Scholar 

  • Lee RS, Gimenez F, Hoogi A, Miyake KK, Gorovoy M, Rubin DL (2017) A curated mammography data set for use in computer-aided detection and diagnosis research. Sci Data 4(1):1–9

  • Lei Y, He X, Yao J, Wang T, Wang L, Li W, Curran WJ, Liu T, Xu D, Yang X (2021) Breast tumor segmentation in 3D automatic breast ultrasound using Mask scoring R-CNN. Med Phys 48(1):204–214

    Google Scholar 

  • Li J, Sang N, Gao C (2016) LEDTD: Local edge direction and texture descriptor for face recognition. Signal Process: Image Commun 41:40–45

    Google Scholar 

  • Li Y, Liu Y, Huang L, Wang Z, Luo J (2022) Deep weakly-supervised breast tumor segmentation in ultrasound images with explicit anatomical constraints. Med Image Anal 76:102315

    Google Scholar 

  • Melekoodappattu JG, Dhas AS, Kandathil BK, Adarsh KS (2022) Breast cancer detection in mammogram: combining modified CNN and texture feature based approach. J Ambient Intell Humaniz Comput 1:1–10

    Google Scholar 

  • Mousavi SM, Asgharzadeh-Bonab A, Ranjbarzadeh R (2021) Time-frequency analysis of EEG Signals and GLCM features for depth of anesthesia monitoring. Comput Intell Neurosci 2021:1–14

    Google Scholar 

  • Munir K, Elahi H, Ayub A, Frezza F, Rizzi A (2019) Cancer diagnosis using deep learning: a bibliographic review. Cancers 11(9):1235

    Google Scholar 

  • Murtaza G, Shuib L, Abdul Wahab AW, Mujtaba G, Mujtaba G, Nweke HF, Al-garadi MA, Zulfiqar F, Raza G, Azmi NA (2020) Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges. Artif Intell Rev 53(3):1655–1720

    Google Scholar 

  • Olugbara OO, Adetiba E, Oyewole SA (2015) Pixel intensity clustering algorithm for multilevel image segmentation. Math Problems Eng. https://doi.org/10.1155/2015/649802

    Article  Google Scholar 

  • Oyelade ON, Ezugwu AE (2022) A novel wavelet decomposition and transformation convolutional neural network with data augmentation for breast cancer detection using digital mammogram. Sci Rep 12(1):1–22

    Google Scholar 

  • Patil RS, Biradar N (2020) Automated mammogram breast cancer detection using the optimized combination of convolutional and recurrent neural network. Evol Intell. https://doi.org/10.1007/s12065-020-00403-x

    Article  Google Scholar 

  • Peng C, Zhang Y, Meng Y, Yang Y, Qiu B, Cao Y, Zheng J (2022a) LMA-Net: A lesion morphology aware network for medical image segmentation towards breast tumors. Comput Biol Med 147:105685

    Google Scholar 

  • Peng C, Zhang Y, Zheng J, Li B, Shen J, Li M, Liu L, Qiu B, Chen DZ (2022b) IMIIN: An inter-modality information interaction network for 3D multi-modal breast tumor segmentation. Comput Med Imaging Graph 95:102021

    Google Scholar 

  • Qi X, Hu J, Zhang L, Bai S, Yi Z (2020a) Automated segmentation of the clinical target volume in the planning CT for breast cancer using deep neural networks. IEEE Transactions on Cybernetics. https://doi.org/10.1109/TCYB.2020.3012186

    Article  Google Scholar 

  • Qi Y, Yang Z, Lei J, Lian J, Liu J, Feng W, Ma Y (2020b) Morph_SPCNN model and its application in breast density segmentation. Multimedia Tools Appl. https://doi.org/10.1007/s11042-020-09796-4

    Article  Google Scholar 

  • Rahimpour M, Saint Martin MJ, Frouin F, Akl P, Orlhac F, Koole M, Malhaire C (2022) Visual ensemble selection of deep convolutional neural networks for 3D segmentation of breast tumors on dynamic contrast enhanced MRI. Eur Radiol. https://doi.org/10.1007/s00330-022-09113-7

    Article  Google Scholar 

  • Rampun A, Scotney BW, Morrow PJ, Wang H, Winder J (2019) Segmentation of breast MR images using a generalised 2D mathematical model with inflation and deflation forces of active contours. Artif Intell Med 97:44–60

    Google Scholar 

  • Rangayyan RM, Nguyen TM (2007) Fractal analysis of contours of breast masses in mammograms. J Digit Imaging 20(3):223–237

    Google Scholar 

  • Ranjbarzadeh R, Baseri Saadi S (2020) Corrigendum to “Automated liver and tumor segmentation based on concave and convex points using fuzzy c-means and mean shift clustering” [Measurement 150 (2020) 107086]. Measurement 151:107230

    Google Scholar 

  • Ranjbarzadeh R, Saadi SB (2020) Automated liver and tumor segmentation based on concave and convex points using fuzzy c-means and mean shift clustering. Measurement 150:107086

    Google Scholar 

  • Ranjbarzadeh R, Saadi SB, Amirabadi A (2020) LNPSS: SAR Image despeckling based on local and non-local features using patch shape selection and edges linking Measurement. J Int Measure Confed 164:107989

    Google Scholar 

  • Ranjbarzadeh R, Bagherian Kasgari A, Jafarzadeh Ghoushchi S, Anari S, Naseri M, Bendechache M (2021a) Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images. Sci Rep 11(1):10930

    Google Scholar 

  • Ranjbarzadeh R, Jafarzadeh Ghoushchi S, Bendechache M, Amirabadi A, Ab Rahman MN, Baseri Saadi S, Aghamohammadi A, Kooshki Forooshani M (2021b) Lung Infection Segmentation for COVID-19 Pneumonia Based on a Cascade Convolutional Network from CT Images. Biomed Res Int 2021:1–16

    Google Scholar 

  • Ranjbarzadeh R, Dorosti S, Saeid, Ghoushchi J, Safavi S, Razmjooy N, Nazanin, Sarshar T, Anari S, Bendechache M, Ghoushchi SJ, Sarshar NT (2022a) Nerve optic segmentation in CT images using a deep learning model and a texture descriptor. Complex Intelligent Syst 8(4):3543–3557

    Google Scholar 

  • Ranjbarzadeh R, Saeid, Ghoushchi J, Shokofeh, Anari, Safavi S, Nazanin N, Sarshar T, Erfan, Tirkolaee B, Bendechache M, Tirkolaee EB, Ghoushchi SJ, Anari S, Sarshar NT (2022b) A deep learning approach for robust, multi-oriented, and curved text detection. Cognitive Comput 1:1–13

    Google Scholar 

  • Ranjbarzadeh R, Tataei Sarshar N, Jafarzadeh Ghoushchi S, Saleh Esfahani M, Parhizkar M, Pourasad Y, Anari S, Bendechache M (2022c) MRFE-CNN: multi-route feature extraction model for breast tumor segmentation in Mammograms using a convolutional neural network. Ann Oper Res 2022:1–22

    Google Scholar 

  • Ranjbarzadeh R, Caputo A, Tirkolaee EB, Jafarzadeh Ghoushchi S, Bendechache M (2023a) Brain tumor segmentation of MRI images: a comprehensive review on the application of artificial intelligence tools. Comput Biol Med 152:106405

    Google Scholar 

  • Ranjbarzadeh R, Dorosti S, Jafarzadeh Ghoushchi S, Caputo A, Tirkolaee EB, Ali SS, Arshadi Z, Bendechache M (2023b) Breast tumor localization and segmentation using machine learning techniques: Overview of datasets, findings, and methods. Comput Biol Med 152:106443

    Google Scholar 

  • Ranjbarzadeh, R., Zarbakhsh, P., Caputo, A., Tirkolaee, E.B. & Bendechache, M. Brain Tumor Segmentation based on an Optimized Convolutional Neural Network and an Improved Chimp Optimization Algorithm. Expert Systems with Applications 2022d (under review).

  • Saadi SB, Ranjbarzadeh R, Kazemi O, Amirabadi A, Ghoushchi SJ, Kazemi O, Azadikhah S, Bendechache M (2021) Osteolysis: a literature review of basic science and potential computer-based image processing detection methods. Comput Intelligence Neurosci. https://doi.org/10.1155/2021/4196241

    Article  Google Scholar 

  • Salmanmahiny A, Erfani M, Danehkar A, Etemad V (2021) Image texture indices and trend analysis for forest disturbance assessment under wood harvest regimes. Journal of Forestry Research 32:579–587

    Google Scholar 

  • Shen L, He M, Shen N, Yousefi N, Wang C, Liu G (2020) Optimal breast tumor diagnosis using discrete wavelet transform and deep belief network based on improved sunflower optimization method. Biomed Signal Process Control 60:101953

    Google Scholar 

  • Shi P, Zhong J, Rampun A, Wang H (2018) A hierarchical pipeline for breast boundary segmentation and calcification detection in mammograms. Comput Biol Med 96:178–188

    Google Scholar 

  • Singh, A., Ranjbarzadeh, R., Raj, K., Kumar, T. & Roy, A.M. 2023. Understanding EEG signals for subject-wise Definition of Armoni Activities.

  • Sivakumar V, Janakiraman N (2022) An optimal region growing segmentation algorithm with decision tree tumor classifier. Lecture Notes Electrical Eng 783:271–281

    Google Scholar 

  • Soh LK, Tsatsoulis C (1999) Texture analysis of sar sea ice imagery using gray level co-occurrence matrices. IEEE Trans Geosci Remote Sens 37(2):780–795

    Google Scholar 

  • Solaiman, B., Hmida, M., Hamrouni, K. & Boussetta, S. 2018. Breast mass segmentation in mammograms combining fuzzy c-means and active contours. Tenth International Conference on Machine Vision (ICMV 2017), hlm. 102. SPIE.

  • Tataei Sarshar, N., Ranjbarzadeh, R., Jafarzadeh Ghoushchi, S., de Oliveira, G.G., Anari, S., Parhizkar, M. & Bendechache, M. 2023. Glioma Brain Tumor Segmentation in Four MRI Modalities Using a Convolutional Neural Network and Based on a Transfer Learning Method: 386–402.

  • Ting FF, Tan YJ, Sim KS (2019) Convolutional neural network improvement for breast cancer classification. Expert Syst Appl 120:103–115

    Google Scholar 

  • Tsochatzidis L, Koutla P, Costaridou L, Pratikakis I (2021) Integrating segmentation information into CNN for breast cancer diagnosis of mammographic masses. Comput Methods Programs Biomed 200:105913

    Google Scholar 

  • Tuncer T, Dogan S, Ozyurt F (2020) An automated residual exemplar local binary pattern and iterative relieff based corona detection method using lung X-ray image. Chemom Intell Lab Syst 203:104054

    Google Scholar 

  • Turab M, Kumar T, Bendechache M, Saber T (2022) Investigating multi-feature selection and ensembling for audio classification. arXiv. https://doi.org/10.48550/arXiv.2206.07511

    Article  Google Scholar 

  • Wahab N, Khan A, Lee YS (2017) Two-phase deep convolutional neural network for reducing class skewness in histopathological images based breast cancer detection. Comput Biol Med 85:86–97

    Google Scholar 

  • Wu WM, Yang XH, Chen YM, Zhang J, Long D, Yang LJ, Tian CX (2019) Layer-wise pre-training low-rank nmf model for mammogram-based breast tumor classification. J Operations Res Soc China 7(4):515–537

    MathSciNet  MATH  Google Scholar 

  • Xiao, D., Lin, H., Xian, C. & Gao, S. 2011. CAD mesh model segmentation by clustering. Computers and Graphics (Pergamon), hlm. 685–691. Elsevier Ltd.

  • Yu X, Zeng N, Liu S, Zhang YD (2019) Utilization of DenseNet201 for diagnosis of breast abnormality Machine Vision and Applications. Springer Verlag, Berlin, pp 1135–1144

    Google Scholar 

  • Zebari DA, Zeebaree DQ, Abdulazeez AM, Haron H, Hamed HNA (2020) Improved threshold based and trainable fully automated segmentation for breast cancer boundary and pectoral muscle in mammogram images. IEEE Access 8:1–20

    Google Scholar 

  • Zeebaree, D.Q., Haron, H., Abdulazeez, A.M. & Zebari, D.A. 2019. Trainable Model Based on New Uniform LBP Feature to Identify the Risk of the Breast Cancer. 2019 International Conference on Advanced Science and Engineering, ICOASE 2019: 106–111.

  • Zhang J, Saha A, Zhu Z, Mazurowski MA (2019) Hierarchical convolutional neural networks for segmentation of breast tumors in mri with application to radiogenomics. IEEE Trans Med Imaging 38(2):435–447

    Google Scholar 

  • Zhang D, Huang G, Zhang Q, Han J, Han J, Yu Y (2020) Cross-modality deep feature learning for brain tumor segmentation. Pattern Recognit 110:107562

    Google Scholar 

  • Zuluaga-Gomez J, Al Masry Z, Benaggoune K, Meraghni S, Zerhouni N (2020) A CNN-based methodology for breast cancer diagnosis using thermal images. Comput Methods Biomech Biomed Eng: Imaging Visual 9(2):131–145

    Google Scholar 

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Funding

This publication has emanated from research [conducted with the financial support of/supported in part by a grant from Science Foundation Ireland under Grant number No. 18/CRT/6183 and is supported by the ADAPT Centre for Digital Content Technology which is funded under the SFI Research Centres Programme (Grant 13/RC/2106/_P2), Lero SFI Centre for Software (Grant 13/RC/2094/_P2) and is co-funded under the European Regional Development Fund. For the purpose of Open Access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission.

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Authors and Affiliations

  1. School of Computing, Faculty of Engineering and Computing, Dublin City University, Dublin, Ireland

    Ramin Ranjbarzadeh

  2. Faculty of Industrial Engineering, Urmia University of Technology, Urmia, Iran

    Saeid Jafarzadeh Ghoushchi

  3. Department of Engineering, Islamic Azad University, Tehran North Branch, Tehran, Iran

    Nazanin Tataei Sarshar

  4. Department of Industrial Engineering, Istinye University, Istanbul, Turkey

    Erfan Babaee Tirkolaee

  5. Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia

    Sadia Samar Ali

  6. ADAPT SFI Research Centre for AI-Driven Digital Content Technology, School of Computing, Dublin City University, Dublin, Ireland

    Teerath Kumar

  7. Lero & ADAPT Research Centres, School of Computer Science, University of Galway, Galway, Ireland

    Malika Bendechache

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  1. Ramin Ranjbarzadeh
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RR, SJG, TK and NTS wrote the main manuscript text and conducted the simulation section. EBT, SSA and MB reviewed the manuscript and validated the simulations.

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Correspondence to Ramin Ranjbarzadeh or Erfan Babaee Tirkolaee.

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Ranjbarzadeh, R., Jafarzadeh Ghoushchi, S., Tataei Sarshar, N. et al. ME-CCNN: Multi-encoded images and a cascade convolutional neural network for breast tumor segmentation and recognition. Artif Intell Rev 56, 10099–10136 (2023). https://doi.org/10.1007/s10462-023-10426-2

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