Proceedings of International Conference on Applied Innovation in IT
2023/03/09, Volume 11, Issue 1, pp.105-111

Image Segmentation as an Instrument for Setting Attention Regions in Convolutional Neural Networks for Bias Detection Purposes

Bojana Velichkovska, Danijela Efnusheva, Marija Kalendar and Goran Jakimovski

Abstract: Convolutional neural networks (CNNs) are constantly being used for medical image processing with increased application in publicly available datasets and are later being actively applied in medical practice. Therefore, since patient lives are at stake, it is important that the functionality of the neural network is beyond reproach. In this paper, due to dataset availability, we present two lung segmentation approaches using traditional image processing and deep learning methodologies; these approaches can later be used to focus a CNN for image segmentation and classification tasks, with implementations spanning everything from disease diagnosis to demographic and bias analysis. The aim of this paper is to provide a framework for segmentation in medical images of the chest cavity, as a way of applying attention regions and localizing sources of bias in images. Both of the proposed segmentation tools, the traditional image approach using computer tomography scans and the CNN applied to chest X-rays, provide excellent lung segmentation comparable to popular methods in the image processing sphere. This allows for an all-encompassing application of the developed methodology regardless of different image formats, therefore making it widely applicable in setting attention regions for CNNs.

Keywords: Artificial Intelligence, Deep Learning, Medical Image Processing, Convolutional Neural Networks, Attention Regions, Lung Segmentation.

DOI: 10.25673/101925

Download: PDF


  1. S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection Region Proposal Networks,” Advances in Neural Information Processing Systems, vol. 28, 2015.
  2. J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation.,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015.
  3. P. Dutta, P. Upadhyay, M. De, and R. G. Khalkar, “Medical Image Analysis using Deep Convolutional Neural Networks: CNN Architectures and Transfer Learning,” 2020 IC on Inventive Computation Technologies (ICICT), Coimbatore, India, 2020.
  4. P. Bir, and V. E. Balas, “A Review on Medical Image Analysis with Convolutional Neural Networks,” 2020 IEEE IC on Computing, Power and Communication Technologies (GUCON), Greater Noida, India, 2020.
  5. P. Kalyani, S. Srivastava, A. Reddyprasad, R. Krishnamoorthy, S. Arun, and S. Padmapriya, “Medical Image Processing from Large Datasets Using Deep Learning,” 2021 3rd IC on Advances in Computing, Communication Control and Networking (ICAC3N), Greater Noida, India, 2021.
  6. J. E. A. Ovalle, F. A. González, R. Ramos-Pollán, J. L. Oliveira, and M. Á. Guevara-López, “Representation learning for mammography mass lesion classification with convolutional neural networks”, Computer Methods and Programs in Biomedicine, vol. 127, pp. 248-257, 2016.
  7. B. Shetty, R. Fernandes, A.P. Rodrigues, R. Chengoden, S. Bhattacharya, and K. Lakshmanna, “Skin lesion classification of dermoscopic images using machine learning and convolutional neural network,” Sci Rep, vol. 12, no. 1, pp. 18134, 2022.
  8. S. P. Jillella, C. Rohith, S. Shameem and P. S. S. Babu, “ECG Classification For Arrhythmias using CNN & Heart Disease Prediction using Web application,” 2022 First IC on Electrical, Electronics, Information and Communication Technologies (ICEEICT), Trichy, India, 2022.
  9. O. Oktay, E. Ferrante, K. Kamnitsas, M. Heinrich, W. Bai, J. Caballero, et al., “Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation,” IEEE Transactions on Medical Imaging, vol. 37, no. 2, pp. 384-395, 2018.
  10. S. Romano, D. Fucci, G. Scanniello, M. Teresa Baldassarre, B. Turhan, and N. Juristo, “Researcher Bias in Software Engineering Experiments: a Qualitative Investigation,” 2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), Portoroz, Slovenia, 2020.
  11. J. A. Sabin, “Tackling Implicit Bias in Health Care”, New England Journal of Medicine, vol. 387, no. 2, pp. 105-107, 2022.
  12. J. W. Gichoya, I. Banerjee, A. R. Bhimireddy, and et al., “AI recognition of patient race in medical imaging: a modelling study,” in The Lancet Digital Health, vol. 4, no. 6, pp. 406-414, 2022.
  13. O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” Medical Image Computing and Computer-Assisted Intervention, Springer, vol. 9351, pp. 234-241, 2015.
  14. S. Jaeger, S. Candemir, S. Antani, Y. X. Wáng, P. X. Lu, and G. Thoma, “Two public chest X-ray datasets for computer-aided screening of pulmonary diseases,” Quant Imaging Med Surg, vol. 4, no. 6, pp. 475-477, 2014.
  15. E. Colak, F. C. Kitamura, S. B. Hobbs, and et al., “The RSNA Pulmonary Embolism CT Dataset,” Radiology: Artificial Intelligence, vol. 3, no. 2, p. e200254, 2021.
  16. M. M. Jawaid, R. Rajani, P. Liatsis, C. C. Reyes-Aldasoro, and G. Slabaugh, “Improved CTA Coronary Segmentation with a Volume-Specific Intensity Threshold,” Medical Image Understanding and Analysis, pp. 207-218, 2017.
  17. D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization”, 2014.



       - Call for Papers
       - Submission to the Journal
       - Paper Submission
       - Final Paper Submission
       - Important Dates
       - Conference Committee
       - Editorial Board
       - Reviewers
       - Last Proceedings


       - Volume 11, Issue 1 (ICAIIT 2023)
       - Volume 10, Issue 1 (ICAIIT 2022)
       - Volume 9, Issue 1 (ICAIIT 2021)
       - Volume 8, Issue 1 (ICAIIT 2020)
       - Volume 7, Issue 1 (ICAIIT 2019)
       - Volume 7, Issue 2 (ICAIIT 2019)
       - Volume 6, Issue 1 (ICAIIT 2018)
       - Volume 5, Issue 1 (ICAIIT 2017)
       - Volume 4, Issue 1 (ICAIIT 2016)
       - Volume 3, Issue 1 (ICAIIT 2015)
       - Volume 2, Issue 1 (ICAIIT 2014)
       - Volume 1, Issue 1 (ICAIIT 2013)


       ICAIIT 2023
         - Photos
         - Reports

       ICAIIT 2022
         - Message

       ICAIIT 2021
         - Photos
         - Reports

       ICAIIT 2020
         - Photos
         - Reports

       ICAIIT 2019
         - Photos
         - Reports

       ICAIIT 2018
         - Photos
         - Reports






Proceedings of the International Conference on Applied Innovations in IT by Anhalt University of Applied Sciences is licensed under CC BY-SA 4.0

                                                   This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License

           ISSN 2199-8876
           Publisher: Anhalt University of Applied Sciences

Creative Commons License
Except where otherwise noted, all works and proceedings on this site is licensed under Creative Commons Attribution-ShareAlike 4.0 International License.