Hong, M., et al., Multi-class classification of lung diseases using CNN models. Applied Sciences, 2021. 11(19): p. 9289.

Article  CAS  Google Scholar 

Malik, H., et al., CDC_Net: Multi-classification convolutional neural network model for detection of COVID-19, pneumothorax, pneumonia, lung Cancer, and tuberculosis using chest X-rays. Multimedia Tools and Applications, 2023. 82(9): p. 13855-13880.

Article  PubMed  Google Scholar 

Wang, W., et al., Detection of SARS-CoV-2 in different types of clinical specimens. Jama, 2020. 323(18): p. 1843-1844.

CAS  PubMed  PubMed Central  Google Scholar 

Priyadarsini, M.J.P., et al., Lung diseases detection using various deep learning algorithms. Journal of healthcare engineering, 2023. 35636962023.

Huang, C., et al., Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The lancet, 2020. 395(10223): p. 497-506.

Article  CAS  Google Scholar 

Khatri, A., et al. Pneumonia identification in chest X-ray images using EMD. in Trends in Communication, Cloud, and Big Data. In: Sarma, H., Bhuyan, B., Borah, S., Dutta, N. (eds) Trends in Communication, Cloud, and Big Data. Lecture Notes in Networks and Systems, vol 99. Springer, Singapore.

Chen, L., P. Chen, and Z. Lin, Artificial intelligence in education: A review. Ieee Access, 2020. 8: p. 75264-75278.

Article  Google Scholar 

Alshmrani, G.M.M., et al., A deep learning architecture for multi-class lung diseases classification using chest X-ray (CXR) images. Alexandria Engineering Journal, 2023. 64: p. 923-935.

Article  Google Scholar 

Asuntha, A. and A. Srinivasan, Deep learning for lung Cancer detection and classification. Multimedia Tools and Applications, 2020. 79(11): p. 7731-7762.

Article  Google Scholar 

Iman, M., H.R. Arabnia, and K. Rasheed, A review of deep transfer learning and recent advancements. Technologies, 2023. 11(2): p. 40.

Article  Google Scholar 

Mubarak, D., Classification of early stages of esophageal cancer using transfer learning. Irbm, 2022. 43(4): p. 251-258.

Article  Google Scholar 

Tadesse, G.A., et al. Cardiovascular disease diagnosis using cross-domain transfer learning. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany, 2019. p. 4262-4265.

Aderghal, K., et al. Classification of Alzheimer disease on imaging modalities with deep CNNs using cross-modal transfer learning. 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS), Karlstad, Sweden, 2018. p. 345-350.

Maghdid, H.S., et al. Diagnosing COVID-19 pneumonia from X-ray and CT images using deep learning and transfer learning algorithms. Multimodal image exploitation and learning 2021. 11734: p.99-110.

Liu, C., et al. TX-CNN: Detecting tuberculosis in chest X-ray images using convolutional neural network. IEEE international conference on image processing (ICIP). 2017. pp. 2314-2318.

Tan, T., et al., Optimize transfer learning for lung diseases in bronchoscopy using a new concept: sequential fine-tuning. IEEE journal of translational engineering in health and medicine, 2018. 6: p. 1-8.

Article  Google Scholar 

Nasrullah, N., et al., Automated lung nodule detection and classification using deep learning combined with multiple strategies. Sensors, 2019. 19(17): p. 3722.

Article  PubMed  PubMed Central  Google Scholar 

Chen, K.-C., et al., Diagnosis of common pulmonary diseases in children by X-ray images and deep learning. Scientific Reports, 2020. 10(1): p. 17374.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Hwa, S.K.T., et al., Tuberculosis detection using deep learning and contrastenhanced canny edge detected X-Ray images. IAES International Journal of Artificial Intelligence, 2020. 9(4): p. 713.

Google Scholar 

Akter, S., et al., COVID-19 Detection Using Deep Learning Algorithm on Chest X-ray Images. Biology 2021. 10(11): p. 1174.

Tian, Y. and X. Yang. A Two-Stage Deep Learning Strategy for Pneumothorax Classification. International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME). 2021. p. 1-6.

Humayun, M., et al. A transfer learning approach with a convolutional neural network for the classification of lung carcinoma. Healthcare. 2022. 10: p. 1058.

Sultana, S., A. Pramanik, and M.S. Rahman. Lung Disease Classification Using Deep Learning Models from Chest X-ray Images. 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT). 2023. p. 1-7

Wang, X., et al., ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases. IEEE CVPR 2017.

Joseph, P. C., et al., COVID-19 image data collection. 2020. arXiv:2003.11597.

Wang, L., Z.Q. Lin, and A. Wong, Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. Scientific reports, 2020. 10(1): p. 19549.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Sahu, P., et al., Implementation of CNNs for Crop diseases classification: A comparison of pre-trained model and training from scratch. IJCSNS, 2020. 20(10): p. 206.

Google Scholar 

Sharma, A., R. Kumar, and P. Garg, Deep learning-based prediction model for diagnosing gastrointestinal diseases using endoscopy images. International Journal of Medical Informatics, 2023. 177: p. 105142.

Article  PubMed  Google Scholar 

Sonoda, S. and N. Murata, Neural network with unbounded activation functions is universal approximator. Applied and Computational Harmonic Analysis, 2017. 43(2): p. 233-268.

Article  Google Scholar 

Clevert, D.-A., T. Unterthiner, and S. Hochreiter, Fast and accurate deep network learning by exponential linear units (ELUs). arXiv: Learning. 2015.

Li, S., et al., A physics-informed neural network framework to predict 3D temperature field without labeled data in process of laser metal deposition. Engineering Applications of Artificial Intelligence, 2023. 120: p. 105908.

Article  Google Scholar 

Szegedy, C., et al. Rethinking the inception architecture for computer vision. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016. p. 2818-2826.

Bhatia, Y., et al. Image captioning using Google's inception-resnet-v2 and recurrent neural network. Twelfth International Conference on Contemporary Computing (IC3), India. 2019. p. 1-6.

Rousseau, F., L. Drumetz, and R. Fablet, Residual networks as flows of diffeomorphisms. Journal of Mathematical Imaging and Vision, 2020. 62: p. 365-375.

Article  Google Scholar 

Khan, M.A. and F. Algarni, A healthcare monitoring system for the diagnosis of heart disease in the IoMT cloud environment using MSSO-ANFIS. IEEE access, 2020. 8: p. 122259-122269.

Article  Google Scholar 

Cheng, Y., J. Feng, and K. Jia. A lung disease classification based on feature fusion convolutional neural network with x-ray image enhancement. Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Honolulu, HI, USA, 2018. p. 2032-2035.

Sethi, R., M. Mehrotra, and D. Sethi. Deep learning based diagnosis recommendation for COVID-19 using chest X-rays images. Second International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, 2020. p. 1-4.

Farhan, A.M.Q. and S. Yang, Automatic lung disease classification from the chest X-ray images using hybrid deep learning algorithm. Multimedia Tools and Applications, 2023. 82(25): p. 38561-38587.

Article  Google Scholar