Izvorni znanstveni članak
https://doi.org/10.1080/00051144.2023.2293274
A comparative study of lung disease classification using fine-tuned CXR and chest CT images
M. Shimja
; Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, India
*
K. Kartheeban
; Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, India
* Dopisni autor.
Sažetak
The diagnosis of lung disease is a challenging process that frequently combines clinical information, such as patient symptoms, medical history and test findings, with medical imaging, like
X-rays or CT scans. The classification of lung diseases is very important in healthcare since it helps
with diagnosis and treatment of many different lung diseases. A precise classification of lung
conditions can aid doctors in choosing the best course of action and enhancing patient outcomes. Additionally, accurate classification can aid in evaluating the effectiveness of therapies,
forecasting results and tracking the development of diseases. It is extremely important to accurately classify lung conditions. A comparison of a novel model for lung disease classification from
chest CT and CXR images was presented in this paper. A modified VGG-16 model was used as the
classification model. To improve the performance, a fine-tuning mechanism was added to the
proposed model. The effectiveness of the suggested method is analyzed and compared on two
distinct datasets in terms of performance metrics. The experimental outcomes showed that the
suggested model performs better on the CXR image dataset.
Ključne riječi
Lung diseases; chest X-ray images; chest CT images; deep learning; VGG-16; fine-tuning
Hrčak ID:
322975
URI
Datum izdavanja:
8.1.2024.
Posjeta: 0 *