Skip to main content

Advertisement

SpringerLink
Log in

Sub-classification of invasive and non-invasive cancer from magnification independent histopathological images using hybrid neural networks

  • Research Paper
  • Published:
Evolutionary Intelligence Aims and scope Submit manuscript

Abstract

Histopathology plays a crucial role in helping clinicians to manage patient’s health effectively. To improve diagnostic accuracy from histopathology, this study evaluates the potential of the pre-trained deep-learning-based model on a large dataset for discrimination among sub-classes of breast cancer. A hybrid model is proposed by combining the pre-trained model (Xception and VGG16) along with conventional machine learning classifier to achieve highest accuracies in the classification of breast cancer without considering the magnification factor of the histopathological images. Real-time data augmentation is also applied to the dataset in order to reduce the problem of overfitting. The performance of developed hybrid models is compared for achieving the highest classification accuracies with an optimum running time. It has been found that VGG16 acquires an accuracy, precision, recall, f-score, area under the receiver operating characteristic (AUC), average precision score of 78.67%, 0.76, 0.75, 0.75, 0.86, 0.60 with a running time of 39.72 min when used in conjunction with logistic regression which is further enhanced by the Xception model to 82.45%, 0.83, 0.82, 0.82, 0.90, 0.70 with a running time of 36.57 min. The most optimum performance of the Xception model suggests that it can be utilized as an automated system for the early diagnosis of breast cancer.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Aamir M, Nawi NM, Wahid F, Mahdin HJ (2020) A deep contractive autoencoder for solving multiclass classification problems. Evol Intell 1:1–15

    Google Scholar 

  2. Xie J, Liu R, Luttrell J IV, Zhang CJ (2019) Deep learning based analysis of histopathological images of breast cancer. Front Genet 10:80

    Article  Google Scholar 

  3. Han Z, Wei B, Zheng Y, Yin Y, Li K, Li SJ (2017) Breast cancer multi-classification from histopathological images with structured deep learning model. Sci Rep 7(1):1–10

    Article  Google Scholar 

  4. 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 Imag 1:1–23

    Google Scholar 

  5. Bayramoglu N, Kannala J, Heikkilä J (2016) Deep learning for magnification independent breast cancer histopathology image classification. In: 23rd International conference on pattern recognition (ICPR). IEEE.

  6. Peikari M, Gangeh MJ, Zubovits J, Clarke G, Martel AL (2016) Triaging diagnostically relevant regions from pathology whole slides of breast cancer: a texture based approach. IEEE Trans Med Imaging 35(1):307–315

    Article  Google Scholar 

  7. Sharma S, Mehra R (2018) Breast cancer histology images classification: training from scratch or transfer learning? ICT Express 4(4):247–254

    Article  Google Scholar 

  8. Man R, Yang P, Xu BJ (2020) Classification of breast cancer histopathological images using discriminative patches screened by generative adversarial networks. IEEE Access 8:155362–155377

    Article  Google Scholar 

  9. Spanhol FA, Oliveira LS, Petitjean C, Heutte L (2016) A dataset for breast cancer histopathological image classification. IEEE Trans Biomed Eng 63(7):1455–1462

    Article  Google Scholar 

  10. Naresh A, Venkata Krishna P (2020) An efficient approach for sentiment analysis using machine learning algorithm. Evol Intell

  11. Shi Y, Suk H-I, Gao Y, Lee S-W, Shen DJ, Systems L (2019) Leveraging coupled interaction for multimodal Alzheimer's disease diagnosis. IEEE Trans Neural Netw

  12. Zhou T, Liu M, Thung K-H, Shen DJ (2019) Latent representation learning for Alzheimer’s disease diagnosis with incomplete multi-modality neuroimaging and genetic data. IEEE Trans Med Imaging 38(10):2411–2422

    Article  Google Scholar 

  13. Sharma S, Mehra R (2019) Effect of layer-wise fine-tuning in magnification-dependent classification of breast cancer histopathological image. Vis Comput 1:1–15

    Google Scholar 

  14. Spanhol FA, Oliveira LS, Petitjean C, Heutte L (eds) (2016) Breast cancer histopathological image classification using convolutional neural networks. In: IEEE international joint conference on neural networks (IJCNN)

  15. Sharma S, Mehra R, Kumar S (2020) Optimised CNN in conjunction with efficient pooling strategy for the multi‐classification of breast cancer. IET Image Process 1–11

  16. An F-P, Liu Z-W (2019) Medical image segmentation algorithm based on feedback mechanism CNN. Contrast media and molecular imaging

  17. Bellver Bueno M (2017) Detection-aided medical image segmentation using deep learning: Universitat Politècnica de Catalunya

  18. Zhou T, Ruan S, Canu S (2019) A review: deep learning for medical image segmentation using multi-modality fusion. Array 1:100004

    Article  Google Scholar 

  19. Khan MA, Khan MA, Ahmed F, Mittal M, Goyal LM, Hemanth DJ (2020) Gastrointestinal diseases segmentation and classification based on duo-deep architectures. Pattern Recognit Lett 131:193–204

    Article  Google Scholar 

  20. Shin H-C, Roth HR, Gao M, Lu L, Xu Z, Nogues I et al (2016) Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imag 35(5):1285–1298

    Article  Google Scholar 

  21. Sirinukunwattana K, SeA R, Tsang Y-W, Snead DR, Cree IA, Rajpoot NM (2016) Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans Med Imag 35(5):1196–1206

    Article  Google Scholar 

  22. Xu J, Xiang L, Liu Q, Gilmore H, Wu J, Tang J et al (2016) Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images. IEEE Trans Med Imag 35(1):119–130

    Article  Google Scholar 

  23. Tobore I, Li J, Yuhang L, Al-Handarish Y, Kandwal A, Nie Z et al (2019) Deep learning intervention for health care challenges: some biomedical domain considerations. JMIR mHealth uHealth 7(8):e11966

    Article  Google Scholar 

  24. Das K, Karri SPK, Roy AG, Chatterjee J, Sheet D (eds) (2017). Classifying histopathology whole-slides using fusion of decisions from deep convolutional network on a collection of random multi-views at multi-magnification. In: IEEE 14th international symposium on biomedical imaging (ISBI 2017)

  25. Nanglia P, Kumar S, Mahajan AN, Singh P, Rathee DJ (2020) A hybrid algorithm for lung cancer classification using SVM and neural networks. ICT Express

  26. Nanglia P, Mahajan AN, Rathee DS, Kumar SJIJoME, (2020) Informatics: lung cancer classification using feed forward back propagation neural network for CT images. Int J Med Eng Inf 12(5):447–456

    Google Scholar 

  27. Nanglia P, Kumar S, Rathi D, Singh P (2018) Comparative investigation of different feature extraction techniques for lung cancer detection system. In: International conference on advanced informatics for computing research. Springer, Berlin

  28. Aresta G, Araújo T, Kwok S, Chennamsetty SS, Safwan M, Alex V et al (2019) Bach: grand challenge on breast cancer histology images. Med Image Anal 56:122–139

    Article  Google Scholar 

  29. Akbar S, Peikari M, Salama S, Nofech-Mozes S, Martel A (2017) Transitioning between convolutional and fully connected layers in neural networks: deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, Berlin, pp 143–150

    Google Scholar 

  30. Gupta V, Bhavsar A (eds) (2017) An integrated multi-scale model for breast cancer histopathological image classification with joint colour-texture features.In: International conference on computer analysis of images and patterns. Springer, Berlin

  31. Zhi W, Yueng HWF, Chen Z, Zandavi SM, Lu Z, Chung YY (eds) Using transfer learning with convolutional neural networks to diagnose breast cancer from histopathological images. In: International conference on neural information processing. Springer, Berlin

  32. Khan MA, Kadry S, Alhaisoni M, Nam Y, Zhang Y, Rajinikanth V et al (2020) Computer-aided gastrointestinal diseases analysis from wireless capsule endoscopy: a framework of best features selection. IEEE Access 8:132850–132859

    Article  Google Scholar 

  33. Majid A, Khan MA, Yasmin M, Rehman A, Yousafzai A, Tariq UJ et al (2020) Classification of stomach infections: a paradigm of convolutional neural network along with classical features fusion and selection. Microsc Res Tech 83(5):562–576

    Article  Google Scholar 

  34. Rashid M, Khan MA, Alhaisoni M, Wang S-H, Naqvi SR, Rehman A et al (2020) A sustainable deep learning framework for object recognition using multi-layers deep features fusion and selection. Sustainablity 12(12):5037

    Article  Google Scholar 

  35. Khan MA, Sharif M, Akram T, Bukhari SAC, Nayak RSJPRL (2020) Developed Newton-Raphson based deep features selection framework for skin lesion recognition. Pattern Recognit Lett 129:293–303

    Article  Google Scholar 

  36. Rehman A, Khan MA, Mehmood Z, Saba T, Sardaraz M, Rashid MJ et al (2020) Microscopic melanoma detection and classification: a framework of pixel-based fusion and multilevel features reduction. Microsc Res Tech 83(4):410–423

    Article  Google Scholar 

  37. Shorten C, Khoshgoftaar TM (2019) A survey on image data augmentation for deep learning. J Big Data 6(1):60

    Article  Google Scholar 

  38. Bar Y, Diamant I, Wolf L, Greenspan H (eds) (2015) Deep learning with non-medical training used for chest pathology identification. Medical imaging 2015: computer-aided diagnosis; 2015: international society for optics and photonics

  39. 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  Google Scholar 

  40. Tajbakhsh N, Shin JY, Gurudu SR, Hurst RT, Kendall CB, Gotway MB et al (2016) Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans Med Imag 35(5):1299–1312

    Article  Google Scholar 

  41. Bosch A, Zisserman A, Muñoz X, editors. Scene classification via pLSA. In: European conference on computer vision. Springer, Berlin.

  42. Liao S, Law MW, Chung AC (2009) Dominant local binary patterns for texture classification. IEEE Trans Image Process 18(5):1107–1118

    Article  MathSciNet  MATH  Google Scholar 

  43. Spanhol FA, Oliveira LS, Cavalin PR, Petitjean C, Heutte L (eds) (2017) Deep features for breast cancer histopathological image classification. In: IEEE international conference on systems, man, and cybernetics (SMC). IEEE.

  44. Chollet F (ed) Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition

Download references

Acknowledgements

The authors are immensely thankful to Dr S. S. Patnaik, Director NITTTR, Chandigarh, India, for constant support and guidance throughout the work. Also, the author Dr Sumit Kumar, would like to thank Mr. Ashok Mittal, Chancellor, Lovely Professional University, Punjab, India, for providing all the necessary facilities and support during the execution of the work.

Author information

Authors and Affiliations

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

    Sumit Kumar

  2. Department of Electronics and Communication Engineering, National Institute of Technical Teachers Training and Research, Chandigarh, 160019, India

    Shallu Sharma

Authors
  1. Sumit Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shallu Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Sumit Kumar or Shallu Sharma.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, S., Sharma, S. Sub-classification of invasive and non-invasive cancer from magnification independent histopathological images using hybrid neural networks. Evol. Intel. 15, 1531–1543 (2022). https://doi.org/10.1007/s12065-021-00564-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12065-021-00564-3

Keywords

Search

Navigation