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Classification and Detection of Cancer in Histopathologic Scans of Lymph Node Sections Using Convolutional Neural Network

Neural Processing Letters volume 55, pages 3763–3778 (2023)Cite this article

Abstract

Cancer has been considered one of the major threats to the lives and health of people. The substantial clinical practices show that earlier diagnosis and detection of cancer can provide adaptable treatment methods, increase survivability, and enhance life quality. Moreover, rapid advancements in science, technology, and Computer-Aided Diagnosis systems also provide additional information for robust analysis and examination of medical images. Image processing and machine learning presented promising low-cost approaches for classifying and detecting different cancerous diseases. However, these traditional techniques need extensive pre-processing and laborious manual features extraction methods. Thus, in this paper, we presented a Convolutional Neural Network based method for the classification and detection of metastatic cancer in histopathologic images of lymph node sections. A diagnostic method of cancer in histopathologic images is time consuming and tedious for pathologists because a large tissue area has been examined, and tiny metastasis can be easily ignored. Thus the developed deep learning method can help pathologists in examining the histopathologic scans and assist in decision-making to analyze the disease and cancer staging, which will give consequential opinions in clinical diagnosis. We performed the necessary pre-processing and data augmentation steps to enhance the results and avoid overfitting. The method utilizes low dimensional representations and performs automated, categorical feature extraction and classification, which attain high accuracy for diagnosis of cancer. The method is applied to PatchCamelyon (PCam) data set. Experimental results show good performance with an accuracy rate of 0.94 for the medical image classification and detection task.

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References

  1. Miller KD, Goding Sauer A, Ortiz AP, Fedewa SA, Pinheiro PS, Tortolero-Luna G, Martinez-Tyson D, Jemal A, Siegel RL (2018) Sixty years of CA: a cancer journal for clinicians. CA Cancer J Clin 68(6):425

    Article  Google Scholar 

  2. Tafreshi NK, Kumar V, Morse DL, Gatenby RA (2010) Molecular and functional imaging of breast cancer. Cancer Control 17(3):143

    Article  Google Scholar 

  3. Yenidunya S, Bayrak R, Haltas H (2011) Predictive value of pathological and immunohistochemical parameters for axillary lymph node metastasis in breast carcinoma. Diagn Pathol 6(1):1

    Article  Google Scholar 

  4. Senkus E, Kyriakides S, Ohno S, Penault-Llorca F, Poortmans P, Rutgers E, Zackrisson S, Cardoso F (2015) Primary breast cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 26:v8

    Article  Google Scholar 

  5. Elmore JG, Longton GM, Carney PA, Geller BM, Onega T, Tosteson AN, Nelson HD, Pepe MS, Allison KH, Schnitt SJ et al (2015) Diagnostic concordance among pathologists interpreting breast biopsy specimens. Jama 313(11):1122

    Article  Google Scholar 

  6. Steiner DF, MacDonald R, Liu Y, Truszkowski P, Hipp JD, Gammage C, Thng F, Peng L, Stumpe MC (2018) Impact of deep learning assistance on the histopathologic review of lymph nodes for metastatic breast cancer. Am J Surg Pathol 42(12):1636

    Article  Google Scholar 

  7. Ahmad M, Ahmed I, Khan FA, Qayum F, Aljuaid H (2020) Convolutional neural network-based person tracking using overhead views. Int J Distrib Sens Netw 16(6):1550147720934738

    Article  Google Scholar 

  8. Ahmed I, Ahmad M, Nawaz M, Haseeb K, Khan S, Jeon G (2019) Efficient topview person detector using point based transformation and lookup table. Comput Commun 147:188

    Article  Google Scholar 

  9. Ahmed I, Ahmad M, Rodrigues JJ, Jeon G, Din S (2021) A deep learning-based social distance monitoring framework for COVID-19. Sustain Cities Soc 65:102571

    Article  Google Scholar 

  10. Wasim M, Ahmed I, Ahmad J, Hassan MM (2021) A novel deep learning based automated academic activities recognition in cyber-physical systems. IEEE Access 9:63718

    Article  Google Scholar 

  11. Ahmed I, Jeon G, Piccialli F (2021) A deep-learning-based smart healthcare system for patient discomfort detection at the edge of Internet of things. IEEE Int Things J 8(13):10318–10326

  12. Ahmed I, Ahmad M, Ahmad A, Jeon G (2021) IoT-based crowd monitoring system: using SSD with transfer learning. Comput Electr Eng 93:107226

    Article  Google Scholar 

  13. Ahmad M, Ahmed I, Ullah K, Khan I, Khattak A, Adnan A (2019) A review of feature selection and sentiment analysis technique in issues of propaganda. Int J Adv Comput Sci Appl. https://doi.org/10.14569/IJACSA.2019.0100367

    Article  Google Scholar 

  14. Muhammad W, Ahmed I, Ahmad J, Nawaz M, Alabdulkreem E, Ghadi Y (2022) A video summarization framework based on activity attention modeling using deep features for smart campus surveillance system. PeerJ Comput Sci 8:e911

    Article  Google Scholar 

  15. Ahmad M, Ahmed I, Ullah K, Khan I, Khattak A, Adnan A (2019) Energy efficient camera solution for video surveillance. Int J Adv Comput Sci Appl. https://doi.org/10.14569/IJACSA.2019.0100470

    Article  Google Scholar 

  16. Ahmed I, Ahmad A, Piccialli F, Sangaiah AK, Jeon G (2018) Internet of Things (IoT) for smart precision agriculture and farming in rural areas. IEEE Internet Things J 5(3):1598

    Article  Google Scholar 

  17. Ahmed I, Din S, Jeon G, Piccialli F (2020) Exploring deep learning models for overhead view multiple object detection. IEEE Internet Things J 7(7):5737

    Article  Google Scholar 

  18. Ahmed I, Adnan A (2018) A robust algorithm for detecting people in overhead views. Clust Comput 21(1):633. https://doi.org/10.1007/s10586-017-0968-3

    Article  MathSciNet  Google Scholar 

  19. Ahmed I, Ahmad M, Ahmad A, Jeon G, (2021) Top view multiple people tracking by detection using deep SORT and YOLOv3 with transfer learning: within 5G infrastructure. Int J Mach Learn Cybern 12(11):3053–3067

  20. Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJ (2018) Artificial intelligence in radiology. Nat Rev Cancer 18(8):500

    Article  Google Scholar 

  21. Veeling BS, Linmans J, Winkens J, Cohen T, Welling M (2018)

  22. Ahmed I, Jeon G, Chehri A (2022) An IoT-enabled smart health care system for screening of COVID-19 with multi layers features fusion and selection. Computing, pp 1–18

  23. Jalalian A, Mashohor SB, Mahmud HR, Saripan MIB, Ramli ARB, Karasfi B (2013) Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review. Clin Imaging 37(3):420

    Article  Google Scholar 

  24. Velusamy PD, Karandharaj P (2014) In: 2014 International conference on green computing communication and electrical engineering (ICGCCEE), pp 1–6. IEEE

  25. Ahmed I, Ahmad M, Khan FA, Asif M (2020) Comparison of deep-learning-based segmentation models: using top view person images. IEEE Access 8:136361

    Article  Google Scholar 

  26. Ahmed I, Jeon G, Chehri A, Hassan MM (2021) Urban sensing technologies and geospatial big data analytics in internet of things-enabled smart cities. Sustain Cities Soc 70:102908

    Article  Google Scholar 

  27. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097

    Google Scholar 

  28. Spanhol FA, Oliveira LS, Petitjean C, Heutte L (2016) In: 2016 International joint conference on neural networks (IJCNN), pp 2560–2567. IEEE

  29. Wang D, Khosla A, Gargeya R, Irshad H, Beck AH (2016) arXiv preprint arXiv:1606.05718

  30. Molina-Cabello MA, Rodríguez-Rodríguez JA, Thurnhofer-Hemsi K, López-Rubio E (2021) Histopathological image analysis for breast cancer diagnosis by ensembles of convolutional neural networks and genetic algorithms. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp 1–8, IEEE

  31. Bejnordi BE, Veta M, Van Diest PJ, Van Ginneken B, Karssemeijer N, Litjens G, Van Der Laak JA, Hermsen M, Manson QF, Balkenhol M et al (2017) Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. Jama 318(22):2199

    Article  Google Scholar 

  32. Aubreville M, Knipfer C, Oetter N, Jaremenko C, Rodner E, Denzler J, Bohr C, Neumann H, Stelzle F, Maier A (2017) Automatic classification of cancerous tissue in laserendomicroscopy images of the oral cavity using deep learning. Sci Rep 7(1):1

    Article  Google Scholar 

  33. Liu Y, Gadepalli K, Norouzi M, Dahl GE, Kohlberger T, Boyko A, Venugopalan S, Timofeev A, Nelson PQ, Corrado GS et al (2017) arXiv preprint arXiv:1703.02442

  34. Wahab N, Khan A, Lee YS (2019) Transfer learning based deep CNN for segmentation and detection of mitoses in breast cancer histopathological images. Microscopy 68(3):216

    Article  Google Scholar 

  35. Tolkach Y, Dohmgörgen T, Toma M, Kristiansen G (2020) High-accuracy prostate cancer pathology using deep learning. Nat Mach Intell 2(7):411

    Article  Google Scholar 

  36. Ström P, Kartasalo K, Olsson H, Solorzano L, Delahunt B, Berney DM, Bostwick DG, Evans AJ, Grignon DJ, Humphrey PA et al (2020) Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study. Lancet Oncol 21(2):222

    Article  Google Scholar 

  37. Cruz-Roa A, Gilmore H, Basavanhally A, Feldman M, Ganesan S, Shih NN, Tomaszewski J, González FA, Madabhushi A (2017) Accurate and reproducible invasive breast cancer detection in whole-slide images: a deep learning approach for quantifying tumor extent. Sci Rep 7(1):1

    Article  Google Scholar 

  38. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S (2017) Deep learning for quality assessment of retinal OCT images. Nature 542(7639):115

    Article  Google Scholar 

  39. Ker J, Bai Y, Lee HY, Rao J, Wang L (2019) Automated brain histology classification using machine learning. J Clin Neurosci 66:239

    Article  Google Scholar 

  40. Rehman A, Khan MA, Saba T, Mehmood Z, Tariq U, Ayesha N (2021) Microscopic brain tumor detection and classification using 3D CNN and feature selection architecture. Microsc Res Tech 84(1):133

    Article  Google Scholar 

  41. 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

    Article  Google Scholar 

  42. Wang S, Wang T, Yang L, Yang DM, Fujimoto J, Yi F, Luo X, Yang Y, Yao B, Lin S et al (2019) ConvPath: a software tool for lung adenocarcinoma digital pathological image analysis aided by a convolutional neural network. EBioMedicine 50:103

    Article  Google Scholar 

  43. Teramoto A, Tsukamoto T, Kiriyama Y, Fujita H (2017) Automated classification of lung cancer types from cytological images using deep convolutional neural networks. BioMed Res Int

  44. Ahmed I, Ahmad M, Rodrigues JJ, Jeon G (2021) Edge computing-based person detection system for top view surveillance: using CenterNet with transfer learning. Appl Soft Comput 107:107489

    Article  Google Scholar 

  45. Ahmed I, Ahmad M, Jeon G, Piccialli F (2021) A framework for pandemic prediction using big data analytics. Big Data Res 25:100190

    Article  Google Scholar 

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Authors and Affiliations

  1. Centre for Excellence in IT, Institute of Management Sciences, Hayatabad, Peshawar, Pakistan

    Misbah Ahmad

  2. School of Computing and Information Science, Faculty of Science and Engineering, Anglia Ruskin University, East Road, Cambridge, CB1 1PT, England, UK

    Imran Ahmed

  3. Département de génie électrique et génie informatique, Université du Québec à Trois-Rivières, Quebec, Canada

    Messaoud Ahmed Ouameur

  4. School of Electronic Engineering, Xidian University, Xi’an, 710071, China

    Gwanggil Jeon

  5. Department of Embedded Systems Engineering, Incheon National University, Incheon, 22012, Korea

    Gwanggil Jeon

Authors
  1. Misbah Ahmad
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  2. Imran Ahmed
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  3. Messaoud Ahmed Ouameur
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  4. Gwanggil Jeon
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Correspondence to Gwanggil Jeon.

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Ahmad, M., Ahmed, I., Ouameur, M.A. et al. Classification and Detection of Cancer in Histopathologic Scans of Lymph Node Sections Using Convolutional Neural Network. Neural Process Lett 55, 3763–3778 (2023). https://doi.org/10.1007/s11063-022-10928-0

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  • DOI: https://doi.org/10.1007/s11063-022-10928-0

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