Manuscript received August 29, 2022; revised December 26, 2022; accepted January 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.
Index Terms—Convolutional Neural Network (CNN), deep
learning, CapsNet model, X-ray image diagnoses
Fady Tawfik was with the Department of Computer Science, Middle
Tennessee State University, Murfreesboro, TN, USA. E-mail:
ft2p@mtmail.mtsu.edu (F.T.)
Yi Gu is with the Department of Computer Science, Middle Tennessee
State University, Murfreesboro, TN, USA.
*Correspondence: Yi.Gu@mtsu.edu (Y.G.)
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.
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).