Skip to main content

Numerical Grad-Cam Based Explainable Convolutional Neural Network for Brain Tumor Diagnosis

Abstract

Since the start of the current century, artificial intelligence has gone through critical advances improving the capabilities of intelligent systems. Especially machine learning has changed remarkably and caused the rise of deep learning. Deep learning shows cutting-edge results in terms of even the most advanced, difficult problems. However, that includes a trade-off in terms of interpretability. Although traditional machine learning techniques employ interpretable working mechanisms, hybrid systems and deep learning models are black-box being beyond our understanding capabilities. So, the need for making such systems understandable, additional methods by explainable artificial intelligence (XAI) has been widely developed in last years. In this sense, this study purposes a Convolutional Neural Networks (CNN) model, which runs a new form of Grad-CAM. As providing numerical feedback in addition to the default Grad-CAM, the numerical Grad-CAM (numGrad-CAM) was used within the developed CNN model, in order to have an explainability interface for brain tumor diagnosis. In detail, the numGrad-CAM-CNN model was evaluated via technical and physicians-oriented (human-side) evaluations. The model provided average findings of 97.11% accuracy, 95.58% sensitivity, and 96.81% specificity for the target brain tumor diagnosis setup. Additionally, numGrad-CAM integration provided 90.11% accuracy according to the other CAM variations in the same CNN model. The physicians used the numGrad-CAM-CNN model gave positive responses in terms of using the model for an explainable (and safe) diagnosis decision-making perspective for brain tumors.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Research Data Policy and Data Availability Statements

All data generated or analysed during this study are included in this published article (and its supplementary information files).

References

  1. West DM, Allen JR (2020) Turning Point: Policymaking in the Era of Artificial Intelligence. Brookings Institution Press

  2. Li D, Du Y (2017) Artificial Intelligence with Uncertainty. CRC Press

  3. Janiesch C, Zschech P, Heinrich K (2021) Machine learning and deep learning. Electron Markets 31(3):685–695

    Article  Google Scholar 

  4. Kose U, Watada J, Deperlioglu O, Saucedo JAM (2022) Computational Intelligence for COVID-19 and Future Pandemics. Springer

  5. Plasek A (2016) On the cruelty of really writing a history of machine learning. IEEE Ann Hist Comput 38(4):6–8

    Google Scholar 

  6. Ouyang W, Mueller F, Hjelmare M, Lundberg E, Zimmer C (2019) ImJoy: an open-source computational platform for the deep learning era. Nat Methods 16(12):1199–1200

    Article  Google Scholar 

  7. Zhang Y, Ni Q (2020) Recent advances in quantum machine learning. Quantum Eng 2(1):e34

    Google Scholar 

  8. Kelleher JD (2019) Deep Learning. MIT press

  9. Dargan S, Kumar M, Ayyagari MR, Kumar G (2020) A survey of deep learning and its applications: a new paradigm to machine learning. Arch Comput Methods Eng 27(4):1071–1092

    MathSciNet  Article  Google Scholar 

  10. Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, Farhan L (2021) Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. J Big Data 8(1):1–74

    Article  Google Scholar 

  11. Dhillon A, Verma GK (2020) Convolutional neural network: a review of models, methodologies and applications to object detection. Progress in Artificial Intelligence 9(2):85–112

    Article  Google Scholar 

  12. Ahmed KB, Goldgof GM, Paul R, Goldgof DB, Hall LO (2021) Discovery of a generalization gap of convolutional neural networks on COVID-19 X-rays classification. IEEE Access 9:72970–72979

    Article  Google Scholar 

  13. Sharma N, Jain V, Mishra A (2018) An analysis of convolutional neural networks for image classification. Procedia Comput Sci 132:377–384

    Article  Google Scholar 

  14. David DS, Saravanan D, Jayachandran A (2020) Deep Convolutional Neural Network based Early Diagnosis of multi class brain tumour classification system. Solid State Technology 63(6):3599–3623

    Google Scholar 

  15. Xu S, Liu C, Zong Y, Chen S, Lu Y, Yang L, Zhang C (2019) An early diagnosis of oral cancer based on three-dimensional convolutional neural networks. IEEE Access 7:158603–158611

    Article  Google Scholar 

  16. Janghel RR, Rathore YK (2021) Deep convolution neural network based system for early diagnosis of Alzheimer’s disease. IRBM 42(4):258–267

    Article  Google Scholar 

  17. Mohapatra S, Swarnkar T, Das J (2021) Deep convolutional neural network in medical image processing. Handbook of Deep Learning in Biomedical Engineering. Academic Press, pp 25–60

  18. Kose U, Alzubi J (2021) Deep Learning for Cancer Diagnosis. Springer

  19. Sarvamangala DR, Kulkarni RV (2021) Convolutional neural networks in medical image understanding: a survey.Evolutionary Intelligence,1–22

  20. Gaur M, Faldu K, Sheth A (2021) Semantics of the black-box: Can knowledge graphs help make deep learning systems more interpretable and explainable? IEEE Internet Comput 25(1):51–59

    Article  Google Scholar 

  21. Yang G, Ye Q, Xia J (2022) Unbox the black-box for the medical explainable ai via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond. Inform Fusion 77:29–52

    Article  Google Scholar 

  22. Deperlioglu O, Kose U, Gupta D, Khanna A, Giampaolo F, Fortino G (2022) Explainable framework for Glaucoma diagnosis by image processing and convolutional neural network synergy: Analysis with doctor evaluation. Future Generation Computer Systems 129:152–169

    Article  Google Scholar 

  23. Kenny EM, Ford C, Quinn M, Keane MT (2021) Explaining black-box classifiers using post-hoc explanations-by-example: The effect of explanations and error-rates in XAI user studies. Artif Intell 294:103459

    MathSciNet  Article  Google Scholar 

  24. Arrieta AB, Díaz-Rodríguez N, Del Ser J, Bennetot A, Tabik S, Barbado A, Herrera F (2020) Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Inform Fusion 58:82–115

    Article  Google Scholar 

  25. Meske C, Bunde E, Schneider J, Gersch M (2022) Explainable artificial intelligence: objectives, stakeholders, and future research opportunities. Inform Syst Manage 39(1):53–63

    Article  Google Scholar 

  26. Gunning D, Stefik M, Choi J, Miller T, Stumpf S, Yang GZ (2019) XAI—Explainable artificial intelligence. Sci Rob 4(37):eaay7120

    Article  Google Scholar 

  27. Angelov PP, Soares EA, Jiang R, Arnold NI, Atkinson PM (2021) Explainable artificial intelligence: an analytical review.Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 11(5), e1424

  28. Dong Y, Pan Y, Zhang J, Xu W (2017), July Learning to read chest X-ray images from 16000 + examples using CNN. In 2017 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) (pp. 51–57). IEEE

  29. Fu Y, Mazur TR, Wu X, Liu S, Chang X, Lu Y, Yang D (2018) A novel MRI segmentation method using CNN-based correction network for MRI‐guided adaptive radiotherapy. Med Phys 45(11):5129–5137

    Article  Google Scholar 

  30. Rajagopalan N, Narasimhan V, Vinjimoor K, Aiyer J (2021) Deep CNN framework for retinal disease diagnosis using optical coherence tomography images. J Ambient Intell Humaniz Comput 12(7):7569–7580

    Article  Google Scholar 

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

    Article  Google Scholar 

  32. Kundu R, Basak H, Singh PK, Ahmadian A, Ferrara M, Sarkar R (2021) Fuzzy rank-based fusion of CNN models using Gompertz function for screening COVID-19 CT-scans. Sci Rep 11(1):1–12

    Article  Google Scholar 

  33. Wang P, Kong X, Guo W, Zhang X (2021) Exclusive Feature Constrained Class Activation Mapping for Better Visual Explanation. IEEE Access 9:61417–61428

    Article  Google Scholar 

  34. Ornek AH, Ceylan M (2021) Explainable Artificial Intelligence (XAI): Classification of Medical Thermal Images of Neonates Using Class Activation Maps.Traitement du Signal, 38(5)

  35. Abd-Ellah MK, Awad AI, Khalaf AA, Hamed HF (2019) A review on brain tumor diagnosis from MRI images: Practical implications, key achievements, and lessons learned. Magn Reson Imaging 61:300–318

    Article  Google Scholar 

  36. Naeem A, Anees T, Naqvi RA, Loh WK (2022) A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis. J Personalized Med 12(2):275

    Article  Google Scholar 

  37. Naseer A, Yasir T, Azhar A, Shakeel T, Zafar K (2021) Computer-aided brain tumor diagnosis: performance evaluation of deep learner CNN using augmented brain MRI. International Journal of Biomedical Imaging, 2021

  38. Kose U, Deperlioglu O, Alzubi J, Patrut B (2021) Deep Learning Architectures for Medical Diagnosis. Deep Learning for Medical Decision Support Systems. Springer, Singapore, pp 15–28

    Chapter  Google Scholar 

  39. Alfarzaeai MS, Niu Q, Zhao J, Eshaq RMA, Hu E (2020) Coal/gangue recognition using convolutional neural networks and thermal images. IEEE Access 8:76780–76789

    Article  Google Scholar 

  40. Jalwana MA, Akhtar N, Bennamoun M, Mian A (2021) CAMERAS: Enhanced resolution and sanity preserving class activation mapping for image saliency. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 16327–16336)

  41. Ko, Y. C., Wey, S. Y., Chen, W. T., Chang, Y. F., Chen, M. J., Chiou, S. H., … Lee,C. Y. (2020). Deep learning assisted detection of glaucomatous optic neuropathy and potential designs for a generalizable model. Plos One, 15(5), e0233079

  42. Phan TMN, Nguyen HT (2021) Clinical Decision Support Systems for Pneumonia Diagnosis Using Gradient-Weighted Class Activation Mapping and Convolutional Neural Networks. Soft Computing: Biomedical and Related Applications. Springer, Cham, pp 81–92

    Chapter  Google Scholar 

  43. Sun Y, Dai S, Li J, Zhang Y, Li X (2019) Tooth-marked tongue recognition using gradient-weighted class activation maps. Future Internet 11(2):45

    Article  Google Scholar 

  44. Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE international conference on computer vision (pp. 618–626)

  45. Lloyd CT, Sorichetta A, Tatem AJ (2017) High resolution global gridded data for use in population studies. Sci Data 4(1):1–17

    Article  Google Scholar 

  46. Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J.,… Van Leemput, K. (2014). The multimodal brain tumor image segmentation benchmark(BRATS). IEEE Transactions on Medical Imaging, 34(10), 1993–2024

  47. Wong HB, Lim GH (2011) Measures of diagnostic accuracy: sensitivity, specificity, PPV and NPV. Proceedings of Singapore Healthcare, 20(4), 316–318

  48. Pinchi V, Pradella F, Vitale G, Rugo D, Nieri M, Norelli GA (2016) Comparison of the diagnostic accuracy, sensitivity and specificity of four odontological methods for age evaluation in Italian children at the age threshold of 14 years using ROC curves. Med Sci Law 56(1):13–18

    Article  Google Scholar 

  49. Chattopadhay A, Sarkar A, Howlader P, Balasubramanian VN (2018) Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks. In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 839–847). IEEE

  50. Wang, H., Wang, Z., Du, M., Yang, F., Zhang, Z., Ding, S., … Hu, X. (2020). Score-CAM:Score-weighted visual explanations for convolutional neural networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (pp. 24–25)

  51. Wang G, Li W, Ourselin S, Vercauteren T (2019) Automatic brain tumor segmentation based on cascaded convolutional neural networks with uncertainty estimation. Front Comput Neurosci 13:56

    Article  Google Scholar 

  52. Shaikh M, Anand G, Acharya G, Amrutkar A, Alex V, Krishnamurthi G (2017) Brain tumor segmentation using dense fully convolutional neural network. International MICCAI brainlesion workshop. Springer, Cham, pp 309–319

    Google Scholar 

  53. Chen L, Wu Y, DSouza AM, Abidin AZ, Wismüller A, Xu C (2018) MRI tumor segmentation with densely connected 3D CNN. In Medical Imaging 2018: Image Processing (Vol. 10574, p. 105741F). International Society for Optics and Photonics

  54. Srinivas B, Rao GS (2019) A hybrid CNN-KNN model for MRI brain tumor classification. Int J Recent Technol Eng (IJRTE) ISSN 8(2):2277–3878

    Google Scholar 

  55. Abd-Ellah MK, Awad AI, Hamed HF, Khalaf AA (2019) Parallel deep CNN structure for glioma detection and classification via brain MRI Images. In 2019 31st International Conference on Microelectronics (ICM) (pp. 304–307). IEEE

  56. Islam M, Ren H (2017) Multi-modal pixelnet for brain tumor segmentation. International MICCAI Brainlesion Workshop. Springer, Cham, pp 298–308

    Google Scholar 

  57. Khan MA, Ashraf I, Alhaisoni M, Damaševičius R, Scherer R, Rehman A, Bukhari SAC (2020) Multimodal brain tumor classification using deep learning and robust feature selection: A machine learning application for radiologists. Diagnostics 10(8):565

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jose Antonio Marmolejo-Saucedo.

Ethics declarations

Conflict of Interest

The authors declare that there is no conflict of interest.

Competing Interests

The authors did not receive support from any organization for the submitted work. The authors have no competing interests to declare that are relevant to the content of this article.

Additional information

Publisher’s Note

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

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Marmolejo-Saucedo, J.A., Kose, U. Numerical Grad-Cam Based Explainable Convolutional Neural Network for Brain Tumor Diagnosis. Mobile Netw Appl (2022). https://doi.org/10.1007/s11036-022-02021-6

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11036-022-02021-6

Keywords

  • Grad-cam
  • Explainable artificial intelligence
  • CNN
  • Deep learning
  • Brain tumor
  • Medical diagnosis