Convolutional Neural Networks in Medical Image Understanding

Authors

  • Megha Upreti Graphic Era Hill University, Bhimtal Campus
  • Chitra Pandey Graphic Era Hill University, Bhimtal Campus
  • Ankur Singh Bist Graphic Era Hill University, Bhimtal Campus
  • Buphest Rawat Graphic Era Hill University, Bhimtal Campus
  • Marviola Hardini University of Raharja

DOI:

https://doi.org/10.34306/att.v3i2.188

Keywords:

Feature extraction, CNN, Muli-layer Neural Network, Medical data analysis

Abstract

In the era of social media images/pictures play a vital  role. Facebook, whatsapp, instagram everywhere we see a lot of  pictures nowadays. Along with social media, the pictures play a  very important role in medical science. Medical Image can help  in diagnosis, clinical treatment and teaching tasks. Traditional  classification of images has reached an end because of its time  taking nature and efforts made to extract, select and classify . This problem is solved with the help of CNN(Convolutional neural network).In medical science we have treatment for body  anomalies that were not there before .Using the deep learning  models of CNN we can detect the disease like Cancer ,Lung  Infection and treat it. This article aims to provide a  comprehensive survey of applications of CNNs in medical image  understanding.

References

X. Zhou, Y. Li, and W. Liang, “CNN-RNN based intelligent recommendation for online medical pre-diagnosis support,” IEEE/ACM Trans. Comput. Biol. Bioinforma., 2020.

L. Liu, F.-X. Wu, Y.-P. Wang, and J. Wang, “Multi-Receptive-Field CNN for Semantic Segmentation of Medical Images,” IEEE J. Biomed. Heal. Informatics, vol. 24, no. 11, pp. 3215–3225, 2020.

M. Graziani, T. Lompech, H. Müller, A. Depeursinge, and V. Andrearczyk, “Interpretable CNN Pruning for Preserving Scale-Covariant Features in Medical Imaging,” in Interpretable and Annotation-Efficient Learning for Medical Image Computing, Springer, 2020, pp. 23–32.

P. Dutta, P. Upadhyay, M. De, and R. G. Khalkar, “Medical image analysis using deep convolutional neural networks: Cnn architectures and transfer learning,” in 2020 International Conference on Inventive Computation Technologies (ICICT), 2020, pp. 175–180.

F. Ali et al., “A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion,” Inf. Fusion, vol. 63, pp. 208–222, 2020.

R. Arnaout, L. Curran, Y. Zhao, J. Levine, E. Chinn, and A. Moon-Grady, “Expert-level prenatal detection of complex congenital heart disease from screening ultrasound using deep learning,” medRxiv, 2020.

T.-H. Nguyen, T.-N. Nguyen, and T.-T. Nguyen, “A deep learning framework for heart disease classification in an IoTs-based system,” in A Handbook of Internet of Things in Biomedical and Cyber Physical System, Springer, 2020, pp. 217–244.

I. A. Alshawwa, H. Q. El-Mashharawi, M. Elkahlout, M. O. Al-Shawwa, and S. S. Abu-Naser, “Analyzing Types of Cherry Using Deep Learning,” 2020.

S. N. Pasha, D. Ramesh, S. Mohmmad, and A. Harshavardhan, “Cardiovascular disease prediction using deep learning techniques,” in IOP Conference Series: Materials Science and Engineering, 2020, vol. 981, no. 2, p. 22006.

T. Saba, A. S. Mohamed, M. El-Affendi, J. Amin, and M. Sharif, “Brain tumor detection using fusion of hand crafted and deep learning features,” Cogn. Syst. Res., vol. 59, pp. 221–230, 2020.

M. A. Khan et al., “Multimodal brain tumor classification using deep learning and robust feature selection: A machine learning application for radiologists,” Diagnostics, vol. 10, no. 8, p. 565, 2020.

L. Cai, J. Gao, and D. Zhao, “A review of the application of deep learning in medical image classification and segmentation,” Ann. Transl. Med., vol. 8, no. 11, 2020.

E. K. Wang, C.-M. Chen, M. M. Hassan, and A. Almogren, “A deep learning based medical image segmentation technique in Internet-of-Medical-Things domain,” Futur. Gener. Comput. Syst., vol. 108, pp. 135–144, 2020.

D. Singh, V. Kumar, and M. Kaur, “Classification of COVID-19 patients from chest CT images using multi-objective differential evolution–based convolutional neural networks,” Eur. J. Clin. Microbiol. Infect. Dis., vol. 39, no. 7, pp. 1379–1389, 2020.

A. Vakanski, M. Xian, and P. E. Freer, “Attention-enriched deep learning model for breast tumor segmentation in ultrasound images,” Ultrasound Med. Biol., vol. 46, no. 10, pp. 2819–2833, 2020.

Y. Lei et al., “Deep learning-based breast tumor detection and segmentation in 3D ultrasound image,” in Medical Imaging 2020: Ultrasonic Imaging and Tomography, 2020, vol. 11319, p. 113190Y.

Y. Jiménez-Gaona, M. J. Rodríguez-Álvarez, and V. Lakshminarayanan, “Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review,” Appl. Sci., vol. 10, no. 22, p. 8298, 2020.

A. S. Assiri, S. Nazir, and S. A. Velastin, “Breast tumor classification using an ensemble machine learning method,” J. Imaging, vol. 6, no. 6, p. 39, 2020.

Y. Jiang, M. Yang, S. Wang, X. Li, and Y. Sun, “Emerging role of deep learning?based artificial intelligence in tumor pathology,” Cancer Commun., vol. 40, no. 4, pp. 154–166, 2020.

K. Dev, S. A. Khowaja, A. S. Bist, V. Saini, and S. Bhatia, “Triage of potential COVID-19 patients from chest X-ray images using hierarchical convolutional networks,” Neural Comput. Appl., pp. 1–16, 2021.

K. Arora, A. S. Bist, S. Chaurasia, and R. Prakash, “Analysis of deep learning techniques for COVID-19 detection,” Int J Sci Res Eng Manag i, vol. 4, no. 4, pp. 1–5, 2020.

S. Wang et al., “A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis,” Eur. Respir. J., vol. 56, no. 2, 2020.

A. Esteva et al., “Deep learning-enabled medical computer vision,” NPJ Digit. Med., vol. 4, no. 1, pp. 1–9, 2021.

S. Bhattacharya et al., “Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey,” Sustain. cities Soc., vol. 65, p. 102589, 2021.

L. Zhang et al., “Generalizing deep learning for medical image segmentation to unseen domains via deep stacked transformation,” IEEE Trans. Med. Imaging, vol. 39, no. 7, pp. 2531–2540, 2020.

A. Smailagic et al., “O?MedAL: Online active deep learning for medical image analysis,” Wiley Interdiscip. Rev. Data Min. Knowl. Discov., vol. 10, no. 4, p. e1353, 2020.

Downloads

Published

2021-09-06

How to Cite

Upreti, M., Pandey, C., Bist, A. S., Rawat, B., & Hardini, M. (2021). Convolutional Neural Networks in Medical Image Understanding. Aptisi Transactions on Technopreneurship (ATT), 3(2), 6–12. https://doi.org/10.34306/att.v3i2.188

Issue

Section

Articles