INTEGRATED HYBRID MODEL FOR LUNG DISEASE DETECTION THROUGH DEEP LEARNING
Budati Jaya Lakshmi Narayana
Velagapudi Ramakrishna Siddhartha Engineering College (India)
Gopireddy Krishna Teja Reddy
Velagapudi Ramakrishna Siddhartha Engineering College (India)
https://orcid.org/0009-0003-5014-0554
Sujana Sri Kosaraju
ksujanasri31@gmail.comVelagapudi Ramakrishna Siddhartha Engineering College (India)
Sirigiri Rajeev Choudhary
Velagapudi Ramakrishna Siddhartha Engineering College (India)
Abstract
The burden of lung diseases on world health is substantial, underscoring the vital necessity of timely detection. The VGG16 architecture with additional convolutional layers is used in this study to provide a hybrid method to lung disease classification. It incorporates the Synthetic Minority Over-sampling Technique (SMOTE) to improve model performance in response to the problem of imbalanced class instances. The subset of the NIH Chest X-ray dataset is used to train and assess the model. The designed model classifies the images into 8 different classes of lung diseases. They are Emphysema, Cardiomegaly, Atelectasis, Edema, Consolidation, Mass, Effusion, Pneumothorax. The proposed model delivered accuracy of 96.42% which demonstrates the efficacy in precise classification of lung diseases. The Graphical User Interface (GUI) is integrated for better interaction between the patient and the model. Through improved diagnostic capabilities, this suggested method not only promotes technological innovation but also shows promise for enhancing patient care and health care outcomes.
Keywords:
Lung Disease, Deep learning, VGG16, GUIReferences
[1] Ahmed M. S. et al.: Joint Diagnosis of Pneumonia, COVID-19, and Tuberculosis from Chest X-ray Images: A Deep Learning Approach. Diagnostics 13(15), 2023, 2562 [https://doi.org/10.3390/diagnostics13152562].
DOI: https://doi.org/10.3390/diagnostics13152562
Google Scholar
[2] Albahli S.: Efficient GAN-based Chest Radiographs (CXR) augmentation to diagnose coronavirus disease pneumonia. International Journal of Medical Sciences 17(10), 2020, 1439.
DOI: https://doi.org/10.7150/ijms.46684
Google Scholar
[3] Bhandari M. et al.: Explanatory Classification of CXR Images into COVID-19, Pneumonia, and Tuberculosis Using Deep Learning and XAI. Computers in Biology and Medicine 150, 2022, 106156 [https://doi.org/10.1016/j.compbiomed.2022.106156].
DOI: https://doi.org/10.1016/j.compbiomed.2022.106156
Google Scholar
[4] Farhan A. M. Q., Yang S.: Automatic Lung Disease Classification from the Chest X-ray Images Using Hybrid Deep Learning Algorithm. Multimedia Tools and Applications 82, 2023, 38561–38587 [https://doi.org/10.1007/s11042-023-15047-z].
DOI: https://doi.org/10.1007/s11042-023-15047-z
Google Scholar
[5] Huang G. et al.: Densely Connected Convolutional Networks. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, 2261–2269 [https://doi.org/10.1109/CVPR.2017.243].
DOI: https://doi.org/10.1109/CVPR.2017.243
Google Scholar
[6] Ibrokhimov B., Kang J.-Y.: Deep Learning Model for COVID-19-Infected Pneumonia Diagnosis Using Chest Radiography Images. BioMedInformatics 2, 2022, 654–670 [https://doi.org/10.3390/biomedinformatics2040043].
DOI: https://doi.org/10.3390/biomedinformatics2040043
Google Scholar
[7] Islam K. T. et al.: A Deep Transfer Learning Framework for Pneumonia Detection from Chest X-ray Images. VISIGRAPP (5: VISAPP), 2020.
DOI: https://doi.org/10.5220/0008927002860293
Google Scholar
[8] Karaddi S. H., Sharma L. D.: Automated Multi-class Classification of Lung Diseases from CXR-Images Using Pre-trained Convolutional Neural Networks. Expert Systems with Applications 211, 2023, 118650 [https://doi.org/10.1016/j.eswa.2022.118650].
DOI: https://doi.org/10.1016/j.eswa.2022.118650
Google Scholar
[9] Shamrat F. J. M. et al.: High-Precision Multiclass Classification of Lung Disease through Customized MobileNetV2 from Chest X-ray Images. Computers in Biology and Medicine 155, 2023, 106646 [https://doi.org/10.1016/j.compbiomed.2023.106646].
DOI: https://doi.org/10.1016/j.compbiomed.2023.106646
Google Scholar
[10] NIH Chest X-rays Sample Dataset. Kaggle (accessed: 21 Mar. 2024) [https://www.kaggle.com/datasets/nih-chest-xrays/sample].
Google Scholar
Authors
Budati Jaya Lakshmi NarayanaVelagapudi Ramakrishna Siddhartha Engineering College India
Authors
Gopireddy Krishna Teja ReddyVelagapudi Ramakrishna Siddhartha Engineering College India
https://orcid.org/0009-0003-5014-0554
Authors
Sujana Sri Kosarajuksujanasri31@gmail.com
Velagapudi Ramakrishna Siddhartha Engineering College India
Authors
Sirigiri Rajeev ChoudharyVelagapudi Ramakrishna Siddhartha Engineering College India
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