فهرست نشریات علمی دسترسی آزاد ایران

Background: Chest X-ray (CXR) images are important for diagnosing lung diseases such as pneumonia and tuberculosis. They help medical professionals determine the functions of the heart and lungs. The lungs may change as a result of certain heart issues, and specific disorders may cause anatomical alterations of the heart or lungs. Objectives: To enhance the classification accuracy of CXR images, particularly for identifying eight types of abnormal lung lesions, by applying advanced feature selection and fusion techniques in combination with deep learning models. Results: The binary classification between normal and abnormal achieved 95.9% ± 0.5% accuracy, 95.95% ± 1.09% sensitivity, 93.91% ± 0.34% specificity, 93.91% ± 0.37% precision, and 94.85% ± 0.56% F1 score on the hybrid approach. Multiple classifications of the eight abnormality lesions revealed a promising average area under the curve (AUC) value of 0.872. Conclusion: The combination of deep learning models with advanced feature selection methods is beneficial for not only improving the classification outcomes but also ensuring accuracy and validity.