doi: 10.18178/ijml.2023.13.4.1141
An Advanced Convolutional Neural Network for Detecting Chest X-ray Abnormalities
Fady Tawfik
,
Yi Gu*
- Department of Computer Science, Middle Tennessee State University, Murfreesboro, TN, USA.
- Manuscript receivedAugust 29, 2022
- revisedDecember 26, 2022
- acceptedJanuary 30, 2023
Abstract
In the field of medical images diagnoses, doctors need a valuable second opinion when diagnosing thoracic diseases in chest X-rays. Existing methods of interpreting chest X-ray images classify them into a list of findings without specifying their locations on the images, resulting in uninterpretable results. Convolutional Neural Network (CNN) is a popular model for thoracic diseases diagnoses, which is a deep learning technique that has shown high accuracy in image classification and feature detection. In this work, an advanced CNN model is proposed to identify 14 findings in chest X-rays. For each test image, the intended CNN model should predict a bounding box and class for all findings. The classes range from 0 to 13, with each number corresponding to a specific disease in the dataset. The results have demonstrated that the proposed model outperforms the CapsNet model with an accuracy of 94% in X-ray images classification and labeling.
Keywords
- Convolutional Neural Network (CNN)
- deep learning
- CapsNet model
- X-ray image diagnoses
How to Cite
Fady Tawfik and Yi Gu, "An Advanced Convolutional Neural Network for Detecting Chest X-ray Abnormalities," International Journal of Machine Learning, vol. 13, no. 4, pp. 136-141, 2023. https://doi.org/10.18178/ijml.2023.13.4.1141
Copyright & License
Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).