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Multi-Scale Adaptive Attention Framework for Improved Lung Disease Classification

Year 2025, Volume: 8 Issue: 3, 400 - 409, 30.09.2025

Oladosu Oyebisi Oladimeji , Ayodeji Olusegun Ibitoye

https://doi.org/10.35377/saucis...1635644

Abstract

Lung diseases are a leading cause of morbidity and mortality, underscoring the need for accurate diagnostic tools. Chest X-ray imaging is commonly used for diagnosis, but existing deep learning methods often focus on binary classification and struggle with the complexity of lung diseases. To address these challenges, we developed the Multi-Scale Adaptive Attention Fusion Network (MSAAF-Net), a novel framework designed for enhanced lung disease classification. MSAAF-Net integrates multi-scale feature extraction with self-attention, spatial attention, and channel attention mechanisms, dynamically weighted through a class-aware module. This approach enables the model to capture both fine-grained and large-scale pathological features, improving classification across multiple disease classes. Evaluated on a publicly available chest X-ray dataset using five-fold cross-validation, MSAAF-Net achieved a classification accuracy of 93.53%, an F1-score of 93.76%, and an AUC of 98.33%, surpassing state-of-the-art models. These results demonstrate MSAAF-Net’s ability to effectively manage the complexity of multi-class lung disease classification. The framework enhances automated diagnostic accuracy, supporting better clinical decision-making and advancing AI’s role in lung healthcare.

Keywords

Deep learning Lung disease Attention Fusion Medical Image Analysis

References

  • S. H. Karaddi and L. D. Sharma, “Automated multi-class classification of lung diseases from CXR-images using pre-trained convolutional neural networks,” Expert Syst. Appl., vol. 211, 2023, doi: 10.1016/j.eswa.2022.118650.
  • W. H. O. OMS, “Coronavirus disease (COVID-19) Situation Report – 193,” Coronavirus Dis., no. June, 2022.
  • G. V. E. Rao, R. B., P. N. Srinivasu, M. F. Ijaz, and M. Woźniak, “Hybrid framework for respiratory lung diseases detection based on classical CNN and quantum classifiers from chest X-rays,” Biomed. Signal Process. Control, vol. 88, p. 105567, Feb. 2024, doi: 10.1016/j.bspc.2023.105567.
  • M. H. Al-Sheikh, O. Al Dandan, A. S. Al-Shamayleh, H. A. Jalab, and R. W. Ibrahim, “Multi-class deep learning architecture for classifying lung diseases from chest X-Ray and CT images,” Sci. Rep., vol. 13, no. 1, p. 19373, Nov. 2023, doi: 10.1038/s41598-023-46147-3.
  • C. Huang et al., “Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China,” Lancet, vol. 395, no. 10223, 2020, doi: 10.1016/S0140-6736(20)30183-5.
  • M. O. Wielpütz, C. P. Heußel, F. J. F. Herth, and H.-U. Kauczor, “Radiological Diagnosis in Lung Disease,” Dtsch. Arztebl. Int., 2014, doi: 10.3238/arztebl.2014.0181.
  • L. A. Rousan, E. Elobeid, M. Karrar, and Y. Khader, “Chest x-ray findings and temporal lung changes in patients with COVID-19 pneumonia,” BMC Pulm. Med., vol. 20, no. 1, 2020, doi: 10.1186/s12890-020-01286-5.
  • E. Saad, B. Maamoun, and A. Nimer, “Increased Red Blood Cell Distribution Predicts Severity of Chronic Obstructive Pulmonary Disease Exacerbation,” J. Pers. Med., vol. 13, no. 5, 2023, doi: 10.3390/jpm13050843.
  • S. Resnick et al., “Clinical relevance of the routine daily chest X-Ray in the surgical intensive care unit,” Am. J. Surg., vol. 214, no. 1, 2017, doi: 10.1016/j.amjsurg.2016.09.059.
  • S. Goyal and R. Singh, “Detection and classification of lung diseases for pneumonia and Covid-19 using machine and deep learning techniques,” J. Ambient Intell. Humaniz. Comput., vol. 14, no. 4, 2023, doi: 10.1007/s12652-021-03464-7.
  • G. M. M. Alshmrani, Q. Ni, R. Jiang, H. Pervaiz, and N. M. Elshennawy, “A deep learning architecture for multi-class lung diseases classification using chest X-ray (CXR) images,” Alexandria Eng. J., vol. 64, 2023, doi: 10.1016/j.aej.2022.10.053.
  • T. B. Chandra, K. Verma, B. K. Singh, D. Jain, and S. S. Netam, “Automatic detection of tuberculosis related abnormalities in Chest X-ray images using hierarchical feature extraction scheme,” Expert Syst. Appl., vol. 158, 2020, doi: 10.1016/j.eswa.2020.113514.
  • R. Hooda, A. Mittal, and S. Sofat, “Automated TB classification using ensemble of deep architectures,” Multimed. Tools Appl., vol. 78, no. 22, 2019, doi: 10.1007/s11042-019-07984-5.
  • J. E. Luján-García, C. Yáñez-Márquez, Y. Villuendas-Rey, and O. Camacho-Nieto, “A transfer learning method for pneumonia classification and visualization,” Appl. Sci., vol. 10, no. 8, 2020, doi: 10.3390/APP10082908.
  • O. Stephen, M. Sain, U. J. Maduh, and D. U. Jeong, “An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare,” J. Healthc. Eng., vol. 2019, 2019, doi: 10.1155/2019/4180949.
  • N. Khasawneh, M. Fraiwan, L. Fraiwan, B. Khassawneh, and A. Ibnian, “Detection of covid-19 from chest x-ray images using deep convolutional neural networks,” Sensors, vol. 21, no. 17, 2021, doi: 10.3390/s21175940.
  • M. Rahimzadeh and A. Attar, “A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2,” Informatics Med. Unlocked, vol. 19, 2020, doi: 10.1016/j.imu.2020.100360.
  • S. Vasamsetti, G. S. S. Shreyas, V. Chemboli, and S. Thota, “Comparative Performance Analysis of Deep Learning Models for Lung Disease Prediction using Chest X-Ray Images,” in 6th International Conference on Inventive Computation Technologies, ICICT 2023 - Proceedings, 2023. doi: 10.1109/ICICT57646.2023.10134132.
  • M. Singla, K. S. Gill, D. Upadhyay, and S. Devliyal, “Optimizing Lung Opacity Classification in Chest X-ray Images through Transfer Learning on VGG19 CNN Model,” in 2024 International Conference on Smart Systems for applications in Electrical Sciences (ICSSES), IEEE, May 2024, pp. 1–4. doi: 10.1109/ICSSES62373.2024.10561338.
  • P. Rajpurkar et al., “CheXaid: deep learning assistance for physician diagnosis of tuberculosis using chest x-rays in patients with HIV,” npj Digit. Med., vol. 3, no. 1, p. 115, Sep. 2020, doi: 10.1038/s41746-020-00322-2.
  • M. A. A. Al-qaness et al., “Chest X-ray Images for Lung Disease Detection Using Deep Learning Techniques: A Comprehensive Survey,” Arch. Comput. Methods Eng., vol. 31, no. 6, 2024, doi: 10.1007/s11831-024-10081-y.
  • Y. K. Mali, L. Sharma, K. Mahajan, F. Kazi, P. Kar, and A. Bhogle, “Application of CNN Algorithm on X-Ray Images in COVID-19 Disease Prediction,” in 2023 IEEE International Carnahan Conference on Security Technology (ICCST), IEEE, Oct. 2023, pp. 1–6. doi: 10.1109/ICCST59048.2023.10726852.
  • S. Kordnoori, M. Sabeti, H. Mostafaei, and S. Seyed Agha Banihashemi, “Advances in medical image analysis: A comprehensive survey of lung infection detection,” IET Image Process., vol. 18, no. 13, pp. 3750–3800, Nov. 2024, doi: 10.1049/ipr2.13246.
  • A. U. Ibrahim, M. Ozsoz, S. Serte, F. Al-Turjman, and P. S. Yakoi, “Pneumonia Classification Using Deep Learning from Chest X-ray Images During COVID-19,” Cognit. Comput., vol. 16, no. 4, 2024, doi: 10.1007/s12559-020-09787-5.
  • Z. Tariq, S. K. Shah, and Y. Lee, “Lung Disease Classification using Deep Convolutional Neural Network,” in Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019, 2019. doi: 10.1109/BIBM47256.2019.8983071.
  • A. Ali, Y. Wang, and X. Shi, “Detection of multi‐class lung diseases based on customized neural network,” Comput. Intell., vol. 40, no. 2, Apr. 2024, doi: 10.1111/coin.12649.
  • J. G. Melekoodappattu, A. S. Dhas, B. K. Kandathil, and K. S. Adarsh, “Breast cancer detection in mammogram: combining modified CNN and texture feature based approach,” J. Ambient Intell. Humaniz. Comput., vol. 14, no. 9, 2023, doi: 10.1007/s12652-022-03713-3.
  • F. F. Ting, Y. J. Tan, and K. S. Sim, “Convolutional neural network improvement for breast cancer classification,” Expert Syst. Appl., vol. 120, pp. 103–115, 2019, doi: 10.1016/j.eswa.2018.11.008.
  • P. Ghose, M. A. Uddin, U. K. Acharjee, and S. Sharmin, “Deep viewing for the identification of Covid-19 infection status from chest X-Ray image using CNN based architecture,” Intell. Syst. with Appl., vol. 16, 2022, doi: 10.1016/j.iswa.2022.200130.
  • A. Hatamizadeh et al., “UNETR: Transformers for 3D Medical Image Segmentation,” in Proceedings - 2022 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022, 2022. doi: 10.1109/WACV51458.2022.00181.
  • Z. Han et al., “Accurate Screening of COVID-19 Using Attention-Based Deep 3D Multiple Instance Learning,” IEEE Trans. Med. Imaging, vol. 39, no. 8, 2020, doi: 10.1109/TMI.2020.2996256.
  • L. Li et al., “Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy,” Radiology, vol. 296, no. 2, 2020, doi: 10.1148/radiol.2020200905.
  • N. Sri Kavya, T. shilpa, N. Veeranjaneyulu, and D. Divya Priya, “Detecting Covid19 and pneumonia from chest X-ray images using deep convolutional neural networks,” in Materials Today: Proceedings, 2022. doi: 10.1016/j.matpr.2022.05.199.
  • I. D. Apostolopoulos and T. A. Mpesiana, “Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks,” Phys. Eng. Sci. Med., vol. 43, no. 2, 2020, doi: 10.1007/s13246-020-00865-4.
  • S. Rajaraman, J. Siegelman, P. O. Alderson, L. S. Folio, L. R. Folio, and S. K. Antani, “Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-Rays,” IEEE Access, vol. 8, 2020, doi: 10.1109/ACCESS.2020.3003810.
  • A. A. Abdelhamid, E. Abdelhalim, M. A. Mohamed, and F. Khalifa, “Multi-Classification of Chest X-rays for COVID-19 Diagnosis Using Deep Learning Algorithms,” Appl. Sci., vol. 12, no. 4, 2022, doi: 10.3390/app12042080.
  • Z. Niu, G. Zhong, and H. Yu, “A review on the attention mechanism of deep learning,” Neurocomputing, vol. 452, 2021, doi: 10.1016/j.neucom.2021.03.091.
  • X. Li et al., “Deep Learning Attention Mechanism in Medical Image Analysis: Basics and Beyonds,” Int. J. Netw. Dyn. Intell., 2023, doi: 10.53941/ijndi0201006.
  • M. A. Talukder, “Lung X-Ray Image,” Mendeley Data. 2023.
  • D. Sinha and M. El-Sharkawy, “Thin MobileNet: An Enhanced MobileNet Architecture,” in 2019 IEEE 10th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2019, 2019. doi: 10.1109/UEMCON47517.2019.8993089.
  • L. Gaur, U. Bhatia, N. Z. Jhanjhi, G. Muhammad, and M. Masud, “Medical image-based detection of COVID-19 using Deep Convolution Neural Networks,” in Multimedia Systems, 2023. doi: 10.1007/s00530-021-00794-6.
  • X. Pan et al., “On the Integration of Self-Attention and Convolution,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2022. doi: 10.1109/CVPR52688.2022.00089.
  • X. Zhu, D. Cheng, Z. Zhang, S. Lin, and J. Dai, “An empirical study of spatial attention mechanisms in deep networks,” in Proceedings of the IEEE International Conference on Computer Vision, 2019. doi: 10.1109/ICCV.2019.00679.
  • Q. Wang, B. Wu, P. Zhu, P. Li, W. Zuo, and Q. Hu, “ECA-Net: Efficient channel attention for deep convolutional neural networks,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2020. doi: 10.1109/CVPR42600.2020.01155.
  • O.O. Oladimeji and A. O. J. Ibitoye, "Brain tumor classification using ResNet50-convolutional block attention module", Applied Computing and Informatics, Vol. ahead-of-print No. ahead-of-print, 2023. https://doi.org/10.1108/ACI-09-2023-0022
  • K.C. Chen, H.R. Yu, W.S. Chen et al, “Diagnosis of common pulmonary diseases in children by X-ray images and deep learning”, Sci Rep 10, 17374, 2020. https://doi.org/10.1038/s41598-020-73831-5.
  • F. M. J. M., Shamrat, S. Azam, A. Karim, R. Islam, Z. Tasnim, P. Ghosh, and F. De Boer, “LungNet22: A Fine-Tuned Model for Multiclass Classification and Prediction of Lung Disease Using X-ray Images”, Journal of Personalized Medicine, 12(5), 680, 2022. https://doi.org/10.3390/jpm12050680

Multi-Scale Adaptive Attention Framework for Improved Lung Disease Classification

Year 2025, Volume: 8 Issue: 3, 400 - 409, 30.09.2025

Oladosu Oyebisi Oladimeji , Ayodeji Olusegun Ibitoye

https://doi.org/10.35377/saucis...1635644

Abstract

Lung diseases are a leading cause of morbidity and mortality, underscoring the need for accurate diagnostic tools. Chest X-ray imaging is commonly used for diagnosis, but existing deep learning methods often focus on binary classification and struggle with the complexity of lung diseases. To address these challenges, we developed the Multi-Scale Adaptive Attention Fusion Network (MSAAF-Net), a novel framework designed for enhanced lung disease classification. MSAAF-Net integrates multi-scale feature extraction with self-attention, spatial attention, and channel attention mechanisms, dynamically weighted through a class-aware module. This approach enables the model to capture both fine-grained and large-scale pathological features, improving classification across multiple disease classes. Evaluated on a publicly available chest X-ray dataset using five-fold cross-validation, MSAAF-Net achieved a classification accuracy of 93.53%, an F1-score of 93.76%, and an AUC of 98.33%, surpassing state-of-the-art models. These results demonstrate MSAAF-Net’s ability to effectively manage the complexity of multi-class lung disease classification. The framework enhances automated diagnostic accuracy, supporting better clinical decision-making and advancing AI’s role in lung healthcare.

Keywords

Deep learning Lung disease Attention Fusion Medical Image Analysis

References

  • S. H. Karaddi and L. D. Sharma, “Automated multi-class classification of lung diseases from CXR-images using pre-trained convolutional neural networks,” Expert Syst. Appl., vol. 211, 2023, doi: 10.1016/j.eswa.2022.118650.
  • W. H. O. OMS, “Coronavirus disease (COVID-19) Situation Report – 193,” Coronavirus Dis., no. June, 2022.
  • G. V. E. Rao, R. B., P. N. Srinivasu, M. F. Ijaz, and M. Woźniak, “Hybrid framework for respiratory lung diseases detection based on classical CNN and quantum classifiers from chest X-rays,” Biomed. Signal Process. Control, vol. 88, p. 105567, Feb. 2024, doi: 10.1016/j.bspc.2023.105567.
  • M. H. Al-Sheikh, O. Al Dandan, A. S. Al-Shamayleh, H. A. Jalab, and R. W. Ibrahim, “Multi-class deep learning architecture for classifying lung diseases from chest X-Ray and CT images,” Sci. Rep., vol. 13, no. 1, p. 19373, Nov. 2023, doi: 10.1038/s41598-023-46147-3.
  • C. Huang et al., “Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China,” Lancet, vol. 395, no. 10223, 2020, doi: 10.1016/S0140-6736(20)30183-5.
  • M. O. Wielpütz, C. P. Heußel, F. J. F. Herth, and H.-U. Kauczor, “Radiological Diagnosis in Lung Disease,” Dtsch. Arztebl. Int., 2014, doi: 10.3238/arztebl.2014.0181.
  • L. A. Rousan, E. Elobeid, M. Karrar, and Y. Khader, “Chest x-ray findings and temporal lung changes in patients with COVID-19 pneumonia,” BMC Pulm. Med., vol. 20, no. 1, 2020, doi: 10.1186/s12890-020-01286-5.
  • E. Saad, B. Maamoun, and A. Nimer, “Increased Red Blood Cell Distribution Predicts Severity of Chronic Obstructive Pulmonary Disease Exacerbation,” J. Pers. Med., vol. 13, no. 5, 2023, doi: 10.3390/jpm13050843.
  • S. Resnick et al., “Clinical relevance of the routine daily chest X-Ray in the surgical intensive care unit,” Am. J. Surg., vol. 214, no. 1, 2017, doi: 10.1016/j.amjsurg.2016.09.059.
  • S. Goyal and R. Singh, “Detection and classification of lung diseases for pneumonia and Covid-19 using machine and deep learning techniques,” J. Ambient Intell. Humaniz. Comput., vol. 14, no. 4, 2023, doi: 10.1007/s12652-021-03464-7.
  • G. M. M. Alshmrani, Q. Ni, R. Jiang, H. Pervaiz, and N. M. Elshennawy, “A deep learning architecture for multi-class lung diseases classification using chest X-ray (CXR) images,” Alexandria Eng. J., vol. 64, 2023, doi: 10.1016/j.aej.2022.10.053.
  • T. B. Chandra, K. Verma, B. K. Singh, D. Jain, and S. S. Netam, “Automatic detection of tuberculosis related abnormalities in Chest X-ray images using hierarchical feature extraction scheme,” Expert Syst. Appl., vol. 158, 2020, doi: 10.1016/j.eswa.2020.113514.
  • R. Hooda, A. Mittal, and S. Sofat, “Automated TB classification using ensemble of deep architectures,” Multimed. Tools Appl., vol. 78, no. 22, 2019, doi: 10.1007/s11042-019-07984-5.
  • J. E. Luján-García, C. Yáñez-Márquez, Y. Villuendas-Rey, and O. Camacho-Nieto, “A transfer learning method for pneumonia classification and visualization,” Appl. Sci., vol. 10, no. 8, 2020, doi: 10.3390/APP10082908.
  • O. Stephen, M. Sain, U. J. Maduh, and D. U. Jeong, “An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare,” J. Healthc. Eng., vol. 2019, 2019, doi: 10.1155/2019/4180949.
  • N. Khasawneh, M. Fraiwan, L. Fraiwan, B. Khassawneh, and A. Ibnian, “Detection of covid-19 from chest x-ray images using deep convolutional neural networks,” Sensors, vol. 21, no. 17, 2021, doi: 10.3390/s21175940.
  • M. Rahimzadeh and A. Attar, “A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2,” Informatics Med. Unlocked, vol. 19, 2020, doi: 10.1016/j.imu.2020.100360.
  • S. Vasamsetti, G. S. S. Shreyas, V. Chemboli, and S. Thota, “Comparative Performance Analysis of Deep Learning Models for Lung Disease Prediction using Chest X-Ray Images,” in 6th International Conference on Inventive Computation Technologies, ICICT 2023 - Proceedings, 2023. doi: 10.1109/ICICT57646.2023.10134132.
  • M. Singla, K. S. Gill, D. Upadhyay, and S. Devliyal, “Optimizing Lung Opacity Classification in Chest X-ray Images through Transfer Learning on VGG19 CNN Model,” in 2024 International Conference on Smart Systems for applications in Electrical Sciences (ICSSES), IEEE, May 2024, pp. 1–4. doi: 10.1109/ICSSES62373.2024.10561338.
  • P. Rajpurkar et al., “CheXaid: deep learning assistance for physician diagnosis of tuberculosis using chest x-rays in patients with HIV,” npj Digit. Med., vol. 3, no. 1, p. 115, Sep. 2020, doi: 10.1038/s41746-020-00322-2.
  • M. A. A. Al-qaness et al., “Chest X-ray Images for Lung Disease Detection Using Deep Learning Techniques: A Comprehensive Survey,” Arch. Comput. Methods Eng., vol. 31, no. 6, 2024, doi: 10.1007/s11831-024-10081-y.
  • Y. K. Mali, L. Sharma, K. Mahajan, F. Kazi, P. Kar, and A. Bhogle, “Application of CNN Algorithm on X-Ray Images in COVID-19 Disease Prediction,” in 2023 IEEE International Carnahan Conference on Security Technology (ICCST), IEEE, Oct. 2023, pp. 1–6. doi: 10.1109/ICCST59048.2023.10726852.
  • S. Kordnoori, M. Sabeti, H. Mostafaei, and S. Seyed Agha Banihashemi, “Advances in medical image analysis: A comprehensive survey of lung infection detection,” IET Image Process., vol. 18, no. 13, pp. 3750–3800, Nov. 2024, doi: 10.1049/ipr2.13246.
  • A. U. Ibrahim, M. Ozsoz, S. Serte, F. Al-Turjman, and P. S. Yakoi, “Pneumonia Classification Using Deep Learning from Chest X-ray Images During COVID-19,” Cognit. Comput., vol. 16, no. 4, 2024, doi: 10.1007/s12559-020-09787-5.
  • Z. Tariq, S. K. Shah, and Y. Lee, “Lung Disease Classification using Deep Convolutional Neural Network,” in Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019, 2019. doi: 10.1109/BIBM47256.2019.8983071.
  • A. Ali, Y. Wang, and X. Shi, “Detection of multi‐class lung diseases based on customized neural network,” Comput. Intell., vol. 40, no. 2, Apr. 2024, doi: 10.1111/coin.12649.
  • J. G. Melekoodappattu, A. S. Dhas, B. K. Kandathil, and K. S. Adarsh, “Breast cancer detection in mammogram: combining modified CNN and texture feature based approach,” J. Ambient Intell. Humaniz. Comput., vol. 14, no. 9, 2023, doi: 10.1007/s12652-022-03713-3.
  • F. F. Ting, Y. J. Tan, and K. S. Sim, “Convolutional neural network improvement for breast cancer classification,” Expert Syst. Appl., vol. 120, pp. 103–115, 2019, doi: 10.1016/j.eswa.2018.11.008.
  • P. Ghose, M. A. Uddin, U. K. Acharjee, and S. Sharmin, “Deep viewing for the identification of Covid-19 infection status from chest X-Ray image using CNN based architecture,” Intell. Syst. with Appl., vol. 16, 2022, doi: 10.1016/j.iswa.2022.200130.
  • A. Hatamizadeh et al., “UNETR: Transformers for 3D Medical Image Segmentation,” in Proceedings - 2022 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022, 2022. doi: 10.1109/WACV51458.2022.00181.
  • Z. Han et al., “Accurate Screening of COVID-19 Using Attention-Based Deep 3D Multiple Instance Learning,” IEEE Trans. Med. Imaging, vol. 39, no. 8, 2020, doi: 10.1109/TMI.2020.2996256.
  • L. Li et al., “Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy,” Radiology, vol. 296, no. 2, 2020, doi: 10.1148/radiol.2020200905.
  • N. Sri Kavya, T. shilpa, N. Veeranjaneyulu, and D. Divya Priya, “Detecting Covid19 and pneumonia from chest X-ray images using deep convolutional neural networks,” in Materials Today: Proceedings, 2022. doi: 10.1016/j.matpr.2022.05.199.
  • I. D. Apostolopoulos and T. A. Mpesiana, “Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks,” Phys. Eng. Sci. Med., vol. 43, no. 2, 2020, doi: 10.1007/s13246-020-00865-4.
  • S. Rajaraman, J. Siegelman, P. O. Alderson, L. S. Folio, L. R. Folio, and S. K. Antani, “Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-Rays,” IEEE Access, vol. 8, 2020, doi: 10.1109/ACCESS.2020.3003810.
  • A. A. Abdelhamid, E. Abdelhalim, M. A. Mohamed, and F. Khalifa, “Multi-Classification of Chest X-rays for COVID-19 Diagnosis Using Deep Learning Algorithms,” Appl. Sci., vol. 12, no. 4, 2022, doi: 10.3390/app12042080.
  • Z. Niu, G. Zhong, and H. Yu, “A review on the attention mechanism of deep learning,” Neurocomputing, vol. 452, 2021, doi: 10.1016/j.neucom.2021.03.091.
  • X. Li et al., “Deep Learning Attention Mechanism in Medical Image Analysis: Basics and Beyonds,” Int. J. Netw. Dyn. Intell., 2023, doi: 10.53941/ijndi0201006.
  • M. A. Talukder, “Lung X-Ray Image,” Mendeley Data. 2023.
  • D. Sinha and M. El-Sharkawy, “Thin MobileNet: An Enhanced MobileNet Architecture,” in 2019 IEEE 10th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2019, 2019. doi: 10.1109/UEMCON47517.2019.8993089.
  • L. Gaur, U. Bhatia, N. Z. Jhanjhi, G. Muhammad, and M. Masud, “Medical image-based detection of COVID-19 using Deep Convolution Neural Networks,” in Multimedia Systems, 2023. doi: 10.1007/s00530-021-00794-6.
  • X. Pan et al., “On the Integration of Self-Attention and Convolution,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2022. doi: 10.1109/CVPR52688.2022.00089.
  • X. Zhu, D. Cheng, Z. Zhang, S. Lin, and J. Dai, “An empirical study of spatial attention mechanisms in deep networks,” in Proceedings of the IEEE International Conference on Computer Vision, 2019. doi: 10.1109/ICCV.2019.00679.
  • Q. Wang, B. Wu, P. Zhu, P. Li, W. Zuo, and Q. Hu, “ECA-Net: Efficient channel attention for deep convolutional neural networks,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2020. doi: 10.1109/CVPR42600.2020.01155.
  • O.O. Oladimeji and A. O. J. Ibitoye, "Brain tumor classification using ResNet50-convolutional block attention module", Applied Computing and Informatics, Vol. ahead-of-print No. ahead-of-print, 2023. https://doi.org/10.1108/ACI-09-2023-0022
  • K.C. Chen, H.R. Yu, W.S. Chen et al, “Diagnosis of common pulmonary diseases in children by X-ray images and deep learning”, Sci Rep 10, 17374, 2020. https://doi.org/10.1038/s41598-020-73831-5.
  • F. M. J. M., Shamrat, S. Azam, A. Karim, R. Islam, Z. Tasnim, P. Ghosh, and F. De Boer, “LungNet22: A Fine-Tuned Model for Multiclass Classification and Prediction of Lung Disease Using X-ray Images”, Journal of Personalized Medicine, 12(5), 680, 2022. https://doi.org/10.3390/jpm12050680
There are 47 citations in total.

Details

Primary Language English
Subjects Computer Software, Software Engineering (Other)
Journal Section Research Article
Authors

Oladosu Oyebisi Oladimeji 0000-0001-8835-6156

Ayodeji Olusegun Ibitoye 0000-0002-5631-8507

Early Pub Date September 24, 2025
Publication Date September 30, 2025
Submission Date February 10, 2025
Acceptance Date July 12, 2025
Published in Issue Year 2025 Volume: 8 Issue: 3

Cite

APA Oladimeji, O. O., & Ibitoye, A. O. (2025). Multi-Scale Adaptive Attention Framework for Improved Lung Disease Classification. Sakarya University Journal of Computer and Information Sciences, 8(3), 400-409. https://doi.org/10.35377/saucis...1635644
AMA Oladimeji OO, Ibitoye AO. Multi-Scale Adaptive Attention Framework for Improved Lung Disease Classification. SAUCIS. September 2025;8(3):400-409. doi:10.35377/saucis.1635644
Chicago Oladimeji, Oladosu Oyebisi, and Ayodeji Olusegun Ibitoye. “Multi-Scale Adaptive Attention Framework for Improved Lung Disease Classification”. Sakarya University Journal of Computer and Information Sciences 8, no. 3 (September 2025): 400-409. https://doi.org/10.35377/saucis. 1635644.
EndNote Oladimeji OO, Ibitoye AO (September 1, 2025) Multi-Scale Adaptive Attention Framework for Improved Lung Disease Classification. Sakarya University Journal of Computer and Information Sciences 8 3 400–409.
IEEE O. O. Oladimeji and A. O. Ibitoye, “Multi-Scale Adaptive Attention Framework for Improved Lung Disease Classification”, SAUCIS, vol. 8, no. 3, pp. 400–409, 2025, doi: 10.35377/saucis...1635644.
ISNAD Oladimeji, Oladosu Oyebisi - Ibitoye, Ayodeji Olusegun. “Multi-Scale Adaptive Attention Framework for Improved Lung Disease Classification”. Sakarya University Journal of Computer and Information Sciences 8/3 (September2025), 400-409. https://doi.org/10.35377/saucis. 1635644.
JAMA Oladimeji OO, Ibitoye AO. Multi-Scale Adaptive Attention Framework for Improved Lung Disease Classification. SAUCIS. 2025;8:400–409.
MLA Oladimeji, Oladosu Oyebisi and Ayodeji Olusegun Ibitoye. “Multi-Scale Adaptive Attention Framework for Improved Lung Disease Classification”. Sakarya University Journal of Computer and Information Sciences, vol. 8, no. 3, 2025, pp. 400-9, doi:10.35377/saucis. 1635644.
Vancouver Oladimeji OO, Ibitoye AO. Multi-Scale Adaptive Attention Framework for Improved Lung Disease Classification. SAUCIS. 2025;8(3):400-9.
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