Top

Published in:

10-02-2021

A novel transfer learning approach for the classification of histological images of colorectal cancer

Authors: Elene Firmeza Ohata, João Victor Souza das Chagas, Gabriel Maia Bezerra, Mohammad Mehedi Hassan, Victor Hugo Costa de Albuquerque, Pedro Pedrosa Rebouças Filho

Published in: The Journal of Supercomputing | Issue 9/2021

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Colorectal cancer (CRC) is the second most diagnosed cancer in the United States. It is identified by histopathological evaluations of microscopic images of the cancerous region, relying on a subjective interpretation. The Colorectal Histology dataset used in this study contains 5000 images, made available by the University Medical Center Mannheim. This approach proposes the automatic identification of eight types of tissues found in CRC histopathological evaluation. We apply Transfer Learning from architectures of Convolutional Neural Networks (CNNs). We modify the structures of CNNs to extract features from the images and input them to well-known machine learning methods: Naive Bayes, Multilayer Perceptron, k-Nearest Neighbors, Random Forest, and Support Vector Machine (SVM). We evaluated 108 extractor–classifier combinations. The one that achieved the best results is DenseNet169 with SVM (RBF), reaching an Accuracy of 92.083% and F1-Score of 92.117%. Therefore, our approach is capable of distinguishing tissues found in CRC histopathological evaluation.

previous article Entity-level sentiment prediction in Danmaku video interaction

next article Blockchain-based solutions for security, privacy, and trust management in vehicular networks: a survey

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

We considered the SVM classifier with different kernels as different classifiers.

Abir F, Alva S, Longo WE, Audiso R, Virgo KS, Johnson FE (2006) The postoperative surveillance of patients with colon cancer and rectal cancer. Am J Surg 192(1):100–108. https://doi.org/10.1016/j.amjsurg.2006.01.053CrossRef

Alhindi TJ, Kalra S, Ng KH, Afrin A, Tizhoosh HR (2018) Comparing LBP, HOG and deep features for classification of histopathology images. In: 2018 International Joint Conference on Neural Networks (IJCNN), IEEE, pp 1–7, https://doi.org/10.1109/IJCNN.2018.8489329

Araújo T, Aresta G, Castro E, Rouco J, Aguiar P, Eloy C, Polónia A, Campilho A (2017) Classification of breast cancer histology images using convolutional neural networks. PLoS ONE 12(6):e0177544. https://doi.org/10.1371/journal.pone.0177544CrossRef

Arnold M, Sierra MS, Laversanne M, Soerjomataram I, Jemal A, Bray F (2017) Global patterns and trends in colorectal cancer incidence and mortality. Gut 66(4):683–691. https://doi.org/10.1136/gutjnl-2015-310912CrossRef

Bayramoglu N, Heikkilä J (2016) Transfer learning for cell nuclei classification in histopathology images. In: European Conference on Computer Vision, Springer, pp 532–539

Breiman L (2001) Random forests. Mach Learn 45(1):5–32CrossRef

Chatterjee R, Maitra T, Islam SH, Hassan MM, Alamri A, Fortino G (2019) A novel machine learning based feature selection for motor imagery EEG signal classification in internet of medical things environment. Future Gener Comput Syst 98:419–434. https://doi.org/10.1016/j.future.2019.01.048CrossRef

Choi JY, Yoo TK, Seo JG, Kwak J, Um TT, Rim TH (2017) Multi-categorical deep learning neural network to classify retinal images: a pilot study employing small database. PLoS ONE. https://doi.org/10.1371/journal.pone.0187336CrossRef

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. https://doi.org/10.1109/CVPR.2017.195

10.

Coudray N, Ocampo PS, Sakellaropoulos T, Narula N, Snuderl M, Fenyö D, Moreira AL, Razavian N, Tsirigos A (2018) Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat Med 24(10):1559–1567. https://doi.org/10.1038/s41591-018-0177-5CrossRef

11.

da Nóbrega RVM, Filho PPR, Rodrigues MB, da Silva SPP, Júnior CMJMD, de Albuquerque VHC (2018) Lung nodule malignancy classification in chest computed tomography images using transfer learning and convolutional neural networks. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3895-1CrossRef

12.

Fukunaga K, Narendra PM (1975) A branch and bound algorithm for computing k-nearest neighbors. IEEE Trans Comput 24(7):750–753. https://doi.org/10.1109/T-C.1975.224297CrossRefMATH

13.

Gecer B, Aksoy S, Mercan E, Shapiro LG, Weaver DL, Elmore JG (2018) Detection and classification of cancer in whole slide breast histopathology images using deep convolutional networks. Pattern Recogn 84:345–356. https://doi.org/10.1016/j.patcog.2018.07.022CrossRef

14.

Girard L, Rodriguez-Canales J, Behrens C, Thompson DM, Botros IW, Tang H, Xie Y, Rekhtman N, Travis WD, Wistuba II et al (2016) An expression signature as an aid to the histologic classification of non-small cell lung cancer. Clin Cancer Res 22(19):4880–4889. https://doi.org/10.1158/1078-0432CrossRef

15.

Han D, Liu Q, Fan W (2018) A new image classification method using CNN transfer learning and web data augmentation. Expert Syst Appl 95:43–56. https://doi.org/10.1016/j.eswa.2017.11.028CrossRef

16.

Han T, Nunes VX, De Freitas Souza LF, Marques AG, Silva ICL, Junior MAAF, Sun J, Filho PPR (2020) Internet of medical things-based on deep learning techniques for segmentation of lung and stroke regions in CT scans. IEEE Access 8:71117–71135. https://doi.org/10.1109/ACCESS.2020.2987932CrossRef

17.

Hassan MM, Ullah S, Hossain MS, Alelaiwi A (2020) An end-to-end deep learning model for human activity recognition from highly sparse body sensor data in internet of medical things environment. J Supercomput. https://doi.org/10.1007/s11227-020-03361-4CrossRef

18.

Haykin S (2009) Neural networks and learning machines, vol 3. Pearson Upper Saddle River, NJ, USA

19.

He K, Zhang X, Ren S, Sun J (2016a) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 770–778. https://doi.org/10.1109/CVPR.2016.90

20.

He K, Zhang X, Ren S, Sun J (2016b) Identity mappings in deep residual networks. In: European Conference on Computer Vision, Springer, pp 630–645

21.

Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:170404861

22.

Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 4700–4708. https://doi.org/10.1109/CVPR.2017.243

23.

Hussein S, Cao K, Song Q, Bagci U (2017) Risk stratification of lung nodules using 3d CNN-based multi-task learning. Lecture Notes in Computer Science. Springer, Cham, pp 249–260

24.

Jr Dourado CM, da Silva SP, da Nóbrega RV, Barros AC, Reboucas Filho PP, de Albuquerque VHC (2019) Deep learning IoT system for online stroke detection in skull computed tomography images. Comput Netw 152:25–39. https://doi.org/10.1016/j.comnet.2019.01.019CrossRef

25.

Kather J, Weis CA, Bianconi F, Melchers S, Schad L, Gaiser T, Marx A, Zöllner F (2016a) Multi-class texture analysis in colorectal cancer histology. Sci Rep 6:27988. https://doi.org/10.1038/srep27988CrossRef

26.

Kather JN, Zöllner FG, Bianconi F, Melchers SM, Schad LR, Gaiser T, Marx A, Weis CA (2016b) Collection of textures in colorectal cancer histology. https://doi.org/10.5281/ZENODO.53169

27.

Kather JN, Krisam J, Charoentong P, Luedde T, Herpel E, Weis CA, Gaiser T, Marx A, Valous NA, Ferber D et al (2019) Predicting survival from colorectal cancer histology slides using deep learning: a retrospective multicenter study. PLoS Med. https://doi.org/10.1371/journal.pmed.1002730CrossRef

28.

Kermany DS, Goldbaum M, Cai W, Valentim CC, Liang H, Baxter SL, McKeown A, Yang G, Wu X, Yan F, Dong J, Prasadha MK, Pei J, Ting MY, Zhu J, Li C, Hewett S, Dong J, Ziyar I, Shi A, Zhang R, Zheng L, Hou R, Shi W, Fu X, Duan Y, Huu VA, Wen C, Zhang ED, Zhang CL, Li O, Wang X, Singer MA, Sun X, Xu J, Tafreshi A, Lewis MA, Xia H, Zhang K (2018) Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172(5):1122–1131.e9. https://doi.org/10.1016/j.cell.2018.02.010CrossRef

29.

Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:14126980

30.

Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324. https://doi.org/10.1109/5.726791CrossRef

31.

Loh BCS, Then PHH (2017) Deep learning for cardiac computer-aided diagnosis: benefits, issues & solutions. mHealth 3:45. https://doi.org/10.21037/mhealth.2017.09.01CrossRef

32.

Marques JAL, Cortez PC, Madeiro JPDV, Fong SJ, Schlindwein FS, De Albuquerque VHC (2019) Automatic cardiotocography diagnostic system based on hilbert transform and adaptive threshold technique. IEEE Access 7:73085–73094CrossRef

33.

Marques JAL, Cortez PC, Madeiro JP, de Albuquerque VHC, Fong SJ, Schlindwein FS (2020) Nonlinear characterization and complexity analysis of cardiotocographic examinations using entropy measures. J Supercomput 76(2):1305–1320CrossRef

34.

Olivares R, Munoz R, Soto R, Crawford B, Cárdenas D, Ponce A, Taramasco C (2020) An optimized brain-based algorithm for classifying parkinson’s disease. Appl Sci 10(5):1827. https://doi.org/10.3390/app10051827CrossRef

35.

Orenstein EC, Beijbom O (2017) Transfer learning and deep feature extraction for planktonic image data sets. In: 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE, pp 1082–1088. https://doi.org/10.1109/WACV.2017.125

36.

Paul R, Hawkins SH, Balagurunathan Y, Schabath MB, Gillies RJ, Hall LO, Goldgof DB (2016) Deep feature transfer learning in combination with traditional features predicts survival among patients with lung adenocarcinoma. Tomography 2(4):388. https://doi.org/10.18383/j.tom.2016.00211CrossRef

37.

Rachapudi V, Devi GL (2020) Improved convolutional neural network based histopathological image classification. Evolut Intell 284:1–7

38.

Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115(3):211–252. https://doi.org/10.1007/s11263-015-0816-yMathSciNetCrossRef

39.

Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 4510–4520

40.

Santos MA, Munoz R, Olivares R, Rebouças Filho PP, Del Ser J, de Albuquerque VHC (2020) Online heart monitoring systems on the internet of health things environments: a survey, a reference model and an outlook. Inf Fusion 53:222–239. https://doi.org/10.1016/j.inffus.2019.06.004CrossRef

41.

Sarmento RM, Vasconcelos FFX, Rebouças Filho PP, Wu W, De Albuquerque VHC (2019) Automatic neuroimage processing and analysis in stroke—a systematic review. IEEE Rev Biomed Eng 13:130–155CrossRef

42.

Sarmento RM, Vasconcelos FF, Filho PPR, de Albuquerque VHC (2020) An IoT platform for the analysis of brain CT images based on parzen analysis. Future Gener Comput Syst 105:135–147. https://doi.org/10.1016/j.future.2019.11.033CrossRef

43.

Shaban M, Awan R, Fraz MM, Azam A, Tsang YW, Snead D, Rajpoot NM (2020) Context-aware convolutional neural network for grading of colorectal cancer histology images. IEEE Trans Med Imaging. https://doi.org/10.1109/tmi.2020.2971006CrossRef

44.

Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D, Summers RM (2016) Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35(5):1285–1298. https://doi.org/10.1109/TMI.2016.2528162CrossRef

45.

Siegel RL, Miller KD, Fedewa SA, Ahnen DJ, Meester RG, Barzi A, Jemal A (2017) Colorectal cancer statistics, 2017. CA Cancer J Clin 67(3):177–193. https://doi.org/10.3322/caac.21395CrossRef

46.

Siegel RL, Miller KD, Sauer AG, Fedewa SA, Butterly LF, Anderson JC, Cercek A, Smith RA, Jemal A (2020) Colorectal cancer statistics, 2020. Cancer J Clin CA. https://doi.org/10.3322/caac.21601CrossRef

47.

Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:14091556

48.

Slaoui M, Fiette L (2010) Histopathology procedures: from tissue sampling to histopathological evaluation. Methods in molecular biology. Humana Press, Totowa, pp 69–82

49.

Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1–9. https://doi.org/10.1109/CVPR.2015.7298594

50.

Theodoridis S, Koutroumbas K (2008) Pattern recognition, 4th edn. Academic Press, USAMATH

51.

Tseng KK, Zhang R, Chen CM, Hassan MM (2020) DNetUnet: a semi-supervised CNN of medical image segmentation for super-computing AI service. J Supercomput. https://doi.org/10.1007/s11227-020-03407-7CrossRef

52.

Vapnik VN (1998) Statistical learning theory. Wiley-Interscience, HobokenMATH

53.

Wang C, Shi J, Zhang Q, Ying S (2017) Histopathological image classification with bilinear convolutional neural networks. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, pp 4050–4053

54.

Wang EK, Chen CM, Hassan MM, Almogren A (2020) A deep learning based medical image segmentation technique in internet-of-medical-things domain. Future Gener Comput Syst 108:135–144. https://doi.org/10.1016/j.future.2020.02.054CrossRef

55.

Wang P, Hu X, Li Y, Liu Q, Zhu X (2016) Automatic cell nuclei segmentation and classification of breast cancer histopathology images. Signal Process 122:1–13. https://doi.org/10.1016/j.sigpro.2015.11.011CrossRef

56.

Xu J, Xiang L, Liu Q, Gilmore H, Wu J, Tang J, Madabhushi A (2015) Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images. IEEE Trans Med Imaging 35(1):119–130. https://doi.org/10.1109/TMI.2015.2458702CrossRef

57.

Xu Y, Jia Z, Wang LB, Ai Y, Zhang F, Lai M, Eric I, Chang C (2017) Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features. BMC Bioinform 18(1):281. https://doi.org/10.1186/s12859-017-1685-xCrossRef

58.

Yu KH, Zhang C, Berry GJ, Altman RB, Ré C, Rubin DL, Snyder M (2016) Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat Commun 7:12474. https://doi.org/10.1038/ncomms12474CrossRef

59.

Yu KH, Wang F, Berry GJ, Ré C, Altman RB, Snyder M, Kohane IS (2020) Classifying non-small cell lung cancer types and transcriptomic subtypes using convolutional neural networks. J Am Med Inform Assoc 27(5):757–769. https://doi.org/10.1093/jamia/ocz230CrossRef

60.

Zoph B, Le QV (2016) Neural architecture search with reinforcement learning. arXiv preprint arXiv:161101578

Title: A novel transfer learning approach for the classification of histological images of colorectal cancer
Authors: Elene Firmeza Ohata
João Victor Souza das Chagas
Gabriel Maia Bezerra
Mohammad Mehedi Hassan
Victor Hugo Costa de Albuquerque
Pedro Pedrosa Rebouças Filho
Publication date: 10-02-2021
Publisher: Springer US
Published in: The Journal of Supercomputing / Issue 9/2021
Print ISSN: 0920-8542
Electronic ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-020-03575-6

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Other articles of this Issue 9/2021

GPU accelerated waterpixel algorithm for superpixel segmentation of hyperspectral images

Analysis of the material distribution system of wise information technology of 120 under deep learning

Parallel modeling of wildfires using efficient solvers for ill-conditioned linear systems

Fuzzy rule-based system for energy efficiency in wireless sensor networks

Prediction of cardiovascular risk by measuring carotid intima media thickness from an ultrasound image for type II diabetic mellitus subjects using machine learning and transfer learning techniques

Deploying deep learning approaches to left ventricular non-compaction measurement

Premium Partner