Original scientific paper
https://doi.org/10.1080/00051144.2023.2290737
Empowering diagnosis: an astonishing deep transfer learning approach with fine tuning for precise lung disease classification from CXR 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
* Corresponding author.
Abstract
A fast and precise diagnosis is crucial for the treatment and management of lung diseases, which
are a major global cause of morbidity and mortality. Medical diagnosis and treatment planning
depend heavily on the classification of lung diseases. The correct diagnosis and classification of
many lung disease types is crucial for effective management and treatment. Radiologists with
training evaluate medical images subjectively in order to classify lung diseases using traditional
approaches. This paper proposed an effective technique for classifying lung diseases from CXR
images. For the accurate classification of lung disorders, three distinct fine-tuned models are
proposed. The effectiveness of the suggested fine-tuned models was evaluated using a newly
developed CXR image dataset. According to the experimental findings, the proposed fine-tuned
models outperformed the existing lung disease categorization models the accuracy is 98%. The
suggested approach can effectively be used for lung disease classification.
Keywords
Lung Anatomy; lung diseases; medical imaging; X-ray imaging; computed tomography; deep learning; transfer learning; fine-tuning; performance parameters
Hrčak ID:
322961
URI
Publication date:
12.12.2023.
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