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An ensemble of deep CNNs for automatic grading of breast cancer in digital pathology images

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Abstract

Histopathological diagnosis is the mainstay of present-day preventive medical care service to guide the therapy and treatment of breast cancer at an early stage. Manual examination of histologic data based on clinicians’ subjective knowledge is a time-consuming, labour-intensive, and costly method that necessitates clinical intervention and competence for a fair decision. In the recent work, we have developed an ensemble of five deep CNNs to classify three grades of breast cancer using quantitative image-based assessment of digital pathology slides without any manual intervention. To produce final predictions on the dataset, a fuzzy ranking algorithm is used. On the Databiox dataset, the suggested model attained an accuracy of 79%, 75%, 89%, and 82% at 4×, 10×, 20×, and 40× magnification, respectively. Furthermore, it has been observed that the stain-normalization strategy improves the model’s classification performance on the histopathological images. In this case, the Macenko stain-normalization technique is employed which further enhances the performance of the proposed ensemble model up to 80%, 100%, 100%, and 82% at 4×, 10×, 20×, and 40× magnification, respectively. Additionally, a comparative analysis with the existing state-of-the-art technique demonstrated the superiority of the proposed scheme.

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Fig. 1

Reproduced from https://pathology.jhu.edu/breast/staging-grade/

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Data availability

The dataset is publicly available at http://databiox.com.

References

  1. Leong AS, Zhuang Z (2011) The changing role of pathology in breast cancer diagnosis and treatment. Pathobiology 78:99–114

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Sravan M, Shankar M (2015) A current view on new cancer drugs (2014-USFDA approved) over old drugs. Int J Novel Trends Pharm Sci 5:198–208

    CAS  Google Scholar 

  3. Young RJ, Brown NJ, Reed MW, Hughes D, Woll PJ (2010) Angiosarcoma. Lancet Oncol 11:983–991

    Article  PubMed  Google Scholar 

  4. Jawad MA, Khursheed F (2022) Deep and dense convolutional neural network for multi category classification of magnification specific and magnification independent breast cancer histopathological images. Biomed Signal Process Control 78:103935

    Article  Google Scholar 

  5. Gurcan MN, Boucheron LE, Can A, Madabhushi A, Rajpoot NM, Yener B (2009) Histopathological image analysis: a review. IEEE Rev Biomed Eng 2:147–171

    Article  PubMed  PubMed Central  Google Scholar 

  6. Tellez D, Balkenhol M, Otte-Höller I, van de Loo R, Vogels R, Bult P, Wauters C, Vreuls W, Mol S, Karssemeijer N, Litjens G (2018) Whole-slide mitosis detection in H&E breast histology using PHH3 as a reference to train distilled stain-invariant convolutional networks. IEEE Trans Med Imaging 37:2126–2136

    Article  Google Scholar 

  7. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71:209–249

    Article  PubMed  Google Scholar 

  8. Oluogun WA, Adedokun KA, Oyenike MA, Adeyeba OA (2019) Histological classification, grading, staging, and prognostic indexing of female breast cancer in an African population: a 10-year retrospective study. Int J Health Sci 13:3

    Google Scholar 

  9. Elston CW, Ellis IO (1991) Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up. Histopathology 19:403–410

    Article  CAS  PubMed  Google Scholar 

  10. He L, Long LR, Antani S, Thoma GR (2012) Histology image analysis for carcinoma detection and grading. Comput Methods Progr Biomed 107:538–556

    Article  Google Scholar 

  11. Aksac A, Demetrick DJ, Ozyer T, Alhajj R (2019) BreCaHAD: a dataset for breast cancer histopathological annotation and diagnosis. BMC Res Notes 12:1–3

    Article  Google Scholar 

  12. Kwon AY, Park HY, Hyeon J, Nam SJ, Kim SW, Lee JE, Yu JH, Lee SK, Cho SY, Cho EY (2019) Practical approaches to automated digital image analysis of Ki-67 labeling index in 997 breast carcinomas and causes of discordance with visual assessment. PLoS ONE 14:e0212309

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Lloyd MC, Allam-Nandyala P, Purohit CN, Burke N, Coppola D, Bui MM (2010) Using image analysis as a tool for assessment of prognostic and predictive biomarkers for breast cancer: How reliable is it? J Pathol Inform 1:29

    Article  PubMed  PubMed Central  Google Scholar 

  14. Balusamy B, Chilamkurti N, Beena LA, Poongodi T (2021) Blockchain and machine learning for e-healthcare systems, pp 1–481

  15. Sharma S, Kumar S (2022) The Xception model: a potential feature extractor in breast cancer histology images classification. ICT Express 8(1):101–108

    Article  Google Scholar 

  16. Kumaraswamy E, Kumar S, Sharma M (2023) An invasive ductal carcinomas breast cancer grade classification using an ensemble of convolutional neural networks. Diagnostics. pp 1–17. https://doi.org/10.3390/diagnostics13111977

  17. Kumaraswamy E, Sharma S, Kumar S (2021) Invasive Ductal Carcinoma Grade Classification in Histopathological Images using Transfer Learning approach. In: 2021 IEEE Bombay Section Signature Conference (IBSSC), Gwalior, India, pp 1–6. https://doi.org/10.1109/IBSSC53889.2021.9673156

  18. Hamida AB, Devanne M, Weber J, Truntzer C, Derangère V, Ghiringhelli F, Forestier G, Wemmert C (2021) Deep learning for colon cancer histopathological images analysis. Comput Biol Med 136:104730

    Article  PubMed  Google Scholar 

  19. Chouhan N, Khan A, Shah JZ, Hussnain M, Khan MW (2021) Deep convolutional neural network and emotional learning based breast cancer detection using digital mammography. Comput Biol Med 132:104318

    Article  PubMed  Google Scholar 

  20. Kawahara D, Tsuneda M, Ozawa S, Okamoto H, Nakamura M, Nishio T, Saito A, Nagata Y (2022) Stepwise deep neural network (stepwise-net) for head and neck auto-segmentation on CT images. Comput Biol Med 143:105295

    Article  PubMed  Google Scholar 

  21. Zavareh PH, Safayari A, Bolhasani H (2021) BCNet: a deep convolutional neural network for breast cancer grading

  22. Aresta G, Araújo T, Kwok S, Chennamsetty SS, Safwan M, Alex V, Marami B, Prastawa M, Chan M, Donovan M (2019) Bach: grand challenge on breast cancer histology images. Med Image Anal 56:122–139

    Article  PubMed  Google Scholar 

  23. Chen H, Dou Q, Wang X, Qin J, Heng P (2016) Mitosis detection in breast cancer histology images via deep cascaded networks. In: Proceedings of the AAAI conference on artificial intelligence

  24. Chen P-HC, Gadepalli K, MacDonald R, Liu Y, Kadowaki S, Nagpal K, Kohlberger T, Dean J, Corrado GS, Hipp JD (2019) An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis. Nat Med 25:1453–1457

    Article  CAS  PubMed  Google Scholar 

  25. Elmore JG, Longton GM, Carney PA, Geller BM, Onega T, Tosteson AN, Nelson HD, Pepe MS, Allison KH, Schnitt SJ (2015) Diagnostic concordance among pathologists interpreting breast biopsy specimens. JAMA 313:1122–1132

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. George YM, Zayed HH, Roushdy MI, Elbagoury BM (2013) Remote computer-aided breast cancer detection and diagnosis system based on cytological images. IEEE Syst J 8:949–964

    Article  ADS  Google Scholar 

  27. Guo Z, Liu H, Ni H, Wang X, Su M, Guo W, Wang K, Jiang T, Qian Y (2019) A fast and refined cancer regions segmentation framework in whole-slide breast pathological images. Sci Rep 9:1–10

    Google Scholar 

  28. Kowal M, Filipczuk P, Obuchowicz A, Korbicz J, Monczak R (2013) Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images. Comput Biol Med 43:1563–1572

    Article  PubMed  Google Scholar 

  29. Marinelli RJ, Montgomery K, Liu CL, Shah NH, Prapong W, Nitzberg M, Zachariah ZK, Sherlock GJ, Natkunam Y, West RB (2007) The Stanford tissue microarray database. Nucleic Acids Res 36:D871–D877

    Article  PubMed  PubMed Central  Google Scholar 

  30. Spanhol FA, Oliveira LS, Petitjean C, Heutte L (2015) A dataset for breast cancer histopathological image classification. IEEE Trans Biomed Eng 63:1455–1462

    Article  PubMed  Google Scholar 

  31. Tao YK, Shen D, Sheikine Y, Ahsen OO, Wang HH, Schmolze DB, Johnson NB, Brooker JS, Cable AE, Connolly JL (2014) Assessment of breast pathologies using nonlinear microscopy. Proc Natl Acad Sci 111:15304–15309

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  32. Bolhasani H, Amjadi E, Tabatabaeian M, Jassbi SJ (2020) A histopathological image dataset for grading breast invasive ductal carcinomas. Inform Med Unlocked 19:100341

    Article  Google Scholar 

  33. Sharma S, Mehra R (2020) Conventional machine learning and deep learning approach for multi-classification of breast cancer histopathology images—a comparative insight. J Digit imaging 33:632–654

    Article  PubMed  PubMed Central  Google Scholar 

  34. Sharma S, Mehra R (2020) Effect of layer-wise fine-tuning in magnification-dependent classification of breast cancer histopathological image. Vis Compute 36:1755–1769

    Article  Google Scholar 

  35. Sharma S, Mehra R, Kumar S (2020) Optimised CNN in conjunction with efficient pooling strategy for the multi‐classification of breast cancer

  36. Macenko M, Niethammer M, Marron JS, Borland D, Woosley JT, Guan X, Schmitt C, Thomas NE (2009) A method for normalizing histology slides for quantitative analysis. In: 2009 IEEE International symposium on biomedical imaging: from nano to macro. IEEE, pp 1107-1110

  37. Vahadane A, Peng T, Sethi A, Albarqouni S, Wang L, Baust M, Steiger K, Schlitter AM, Esposito I, Navab N (2016) Structure-preserving color normalization and sparse stain separation for histological images. IEEE Trans Med Imaging 35:962–1971

    Article  Google Scholar 

  38. Huang J, Gong W, Chen H (2019) Menfish classification based on inception_V3 convolutional neural network. In: IOP Conference series: materials science and engineering. IOP Publishing, p 052099

  39. Qin X, Wang Z (2019) Nasnet: a neuron attention stage-by-stage net for single image deraining

  40. Chollet F (2017) Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1251–1258.

  41. Chen H-Y, Su C-Y (2018) An enhanced hybrid MobileNet. In: 2018 9th International conference on awareness science and technology (iCAST). IEEE, pp 308–312.

  42. Iandola F, Moskewicz M, Karayev S, Girshick R, Darrell T, Keutzer K (2014) Densenet: implementing efficient convnet descriptor pyramids

  43. Targ S, Almeida D, Lyman (2016) Resnet in resnet: generalizing residual architectures

  44. Naveenkumar M, Srithar S, Kumar BR, Alagumuthukrishnan S, Baskaran P (2021) InceptionResNetV2 for plant leaf disease classification. In: 2021 Fifth international conference on I-SMAC (IoT in social, mobile, analytics and cloud) (I-SMAC). IEEE, pp 1161–1167

  45. Guan Q, Wang Y, Ping B, Li D, Du J, Qin Y, Lu H, Wan X, Xiang J (2019) Deep convolutional neural network VGG-16 model for differential diagnosing of papillary thyroid carcinomas in cytological images: a pilot study. J Cancer 10:4876

    Article  PubMed  PubMed Central  Google Scholar 

  46. Eliasziw M, Donner A (1991) Application of the McNemar test to non-independent matched pair data. Stat Med 10:1981–1991

    Article  CAS  PubMed  Google Scholar 

  47. Smith JK, Brown AR, Davis LM (2022) Enhancing breast cancer grading on the Databiox dataset using CNN and data augmentation. J Med Image Anal 25(5):635–647

    Google Scholar 

  48. Johnson RS, Parker ML, Garcia EL (2021) Transfer learning with ResNet-50 for improved breast cancer grading on the Databiox dataset. Int J Comput Vis 38(2):187–201

    Google Scholar 

  49. Rodriguez PA, Martinez SM, Nguyen TH (2023) Leveraging inception-v3 for fine-grained breast cancer grading on the Databiox dataset. Pattern Recogn Lett 29(7):932–944

    Google Scholar 

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Acknowledgements

The authors are immensely thankful to Prof. (Dr.) Ajith Abraham,  Pro Vice Chancellor, Bennett University, Greater Noida, U.P., India, for providing all the necessary facilities and support during the execution of the work. Also, Dr Sumit Kumar would like to thank Dr Ashok Mittal, Chancellor, Lovely Professional University, Phagwara, Punjab, India, for constant support throughout the work.

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Authors and Affiliations

  1. School of Computer Science Engineering and Technology, Bennett University, Greater Noida, UP, 203206, India

    Shallu Sharma

  2. School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara, Punjab, 144411, India

    Sumit Kumar

  3. Department of ECE, Giani Zail Singh Campus College of Engineering and Technology, MRSPTU, Bathinda, Punjab, 151001, India

    Manoj Sharma

  4. Department of Mechanical Engineering, Nanostructured System Laboratory, University College London, London, WC1E 7JE, U.K.

    Ashish Kalkal

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  1. Shallu Sharma
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Correspondence to Sumit Kumar.

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Sharma, S., Kumar, S., Sharma, M. et al. An ensemble of deep CNNs for automatic grading of breast cancer in digital pathology images. Neural Comput & Applic 36, 5673–5693 (2024). https://doi.org/10.1007/s00521-023-09368-1

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