Archived Issues

We congratulate you on acceptance of your manuscript.

Browse by Year

Convolutional Neural Network Mediated Detection of Pneumonia

October 14, 2021
Rohan Ghotra

AbstractPneumonia, a fatal lung disease, is caused by infection of Streptococcus pneumoniae; it is detected by chest x-rays that reveal inflammation of the alveoli. However, the efficiency by which it is diagnosed can be improved through the use of artificial intelligence. Convolutional neural networks (CNNs), a form of artificial intelligence, have recently demonstrated enhanced accuracy when classifying images. This study used CNNs to analyze chest x-rays and predict the probability the patient has pneumonia. Furthermore, a comprehensive investigation was conducted, examining the function of various components of the CNN, in the context of pneumonia x-rays. This study was able to achieve significantly high performance, making it viable for clinical implementation. Furthermore, the architecture of the proposed model is applicable to various other diseases, and can thus be used to optimize the disease diagnosis industry.

Keywords: artificial intelligence, disease diagnosis, pneumonia, convolutional neural networks, machine learning


References

  1. Albawi,  S.,  Mohammed,  T.  A.,  &  Al-Zawi,  S.   (2017).   Understanding  of  a  convolutionalneural network.  In 2017 international conference on engineering and technology (icet) (p. 1-6).  doi:  10.1109/ICEngTechnol.2017.8308186
  2. Bebis,  G., & Georgiopoulos,  M.  (1994).  Feed-forward neural networks. IEEE Potentials, 13(4), 27-31.  doi:  10.1109/45.329294
  3. Bjorck,  J.,  Gomes,  C.,  Selman,  B.,  &  Weinberger,  K.  Q.   (2018).   Understanding  batch normalization. arXiv preprint arXiv:1806.02375.
  4. Eckle, K., & Schmidt-Hieber, J. (2019). A comparison of deep networks with relu activation function and linear spline-type methods. Neural Networks,110, 232–242.
  5. Himavathi,  S.,  Anitha,  D., & Muthuramalingam,  A.  (2007).  Feedforward neural network implementation in fpga using layer multiplexing for effective resource utilization. IEEE Transactions on Neural Networks,18(3), 880-888.  doi:  10.1109/TNN.2007.891626
  6. Ho, Y., & Wookey, S.  (2019).  The real-world-weight cross-entropy loss function:  Modeling the costs of mislabeling. IEEE Access,8, 4806–4813.
  7. Huss-Lederman, S., Jacobson, E. M., Johnson, J. R., Tsao, A., & Turnbull, T.  (1996).  Implementation of strassen’s algorithm for matrix multiplication.  In Supercomputing’96:Proceedings of the 1996 acm/ieee conference on supercomputing(pp. 32–32).
  8. Kermany, D., Zhang, K., Goldbaum, M., et al. (2018). Labeled optical coherence tomography (oct) and chest x-ray images for classification. Mendeley data,2(2).
  9. LeCun, Y., Haffner, P., Bottou, L., & Bengio, Y.  (1999).  Object recognition with gradient-based  learning. In Shape, contour and grouping in computer vision (pp.  319–345). Springer.
  10. Liu, K., Kang, G., Zhang, N., & Hou, B. (2018). Breast cancer classification based on fully-connected layer first convolutional neural networks. IEEE Access,6, 23722-23732. doi:10.1109/ACCESS.2018.2817593
  11. Nagi, J., Ducatelle, F., Di Caro, G. A., Cire ╠žsan, D., Meier, U., Giusti, A., . . .  Gambardella, L. M.  (2011).  Max-pooling convolutional neural networks for vision-based hand gesture recognition.  In 2011 ieee international conference on signal and image processing applications (icsipa) (p. 342-347).  doi: 10.1109/ICSIPA.2011.6144164
  12. Ruder, S.  (2016).  An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747.
  13. Yu,  D.,  Wang,  H.,  Chen,  P.,  &  Wei,  Z.   (2014).   Mixed  pooling  for  convolutional  neural networks.   In International conference on rough sets and knowledge technology(pp.364–375).