Enhanced Lung Disease Classification through Deep Learning Fusion with CLBP, Attention Graphs, and Bee Colony Optimization
Keywords:
Artificial Bee Colony optimization, Convolutional Neural Network, Deep Learning, Local Binary Patterns, Lung Diseases.Abstract
The identification and classification of lung diseases based on clinical images is critical to ensuring that physicians diagnose and treat the diseases properly. DeepAttentionCLBP-BeeNet is a new model proposed in this study to improve the accuracy of lung disease classification. In this model, both machine and deep learning algorithms are used to extract relevant information from chest radiographs/CT scan images. Contrast Local Binary Patterns (CLBP) are used for data preprocessing, texture detection, and improving the discriminative capacity of retrieved features. Attention Graphs can capture architectural entailments and temporal boundaries of disease symptoms, which enables more precise classification. When it comes to optimizing the number of parameters, the Bee Colony Optimization algorithm is used. The presented methodology is particularly successful, surpassing convention and improving treatments beyond expectations. Overall, it has a good accuracy of 97% as well as a precision of 96.1%; The specificity was at 96.7% while the recall and sensitivity were both at 97.3%, and F1-score value equal to 97.1%.
Downloads
References
Nelson, Faria., Sofia, Campelos., Vitor, Carvalho. (2023). A Novel Convolutional Neural Network Algorithm for Histopathological Lung Cancer Detection. Applied Sciences, 13(11):6571-6571. doi: 10.3390/app13116571
A. Bhola, D. V. Kiran, D. G. A. Ram, N. Vrajesh and A. Tiwari, "Lung Cancer Detection using Transfer Learning and EfficientNet B2 Architecture," 2023 International Conference on Disruptive Technologies (ICDT), Greater Noida, India, 2023, pp. 395-400, doi: 10.1109/ICDT57929.2023.10150931.
S. Vidyasri and S. Saravanan, "An Automated CHNN Model for the Classification and Detection of Lung Diseases using Transfer Learning," 2023 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), Erode, India, 2023, pp. 180-185, doi: 10.1109/ICSCDS56580.2023.10105034.
Goram Mufarah M. Alshmrani, Qiang Ni, Richard Jiang, Haris Pervaiz, Nada M. Elshennawy, A deep learning architecture for multi-class lung diseases classification using chest X-ray (CXR) images,Alexandria Engineering Journal,Volume 64,2023, Pages 923-935,ISSN 1110-0168, https://doi.org/10.1016/j.aej.2022.10.053.
Kim, S.; Rim, B.; Choi, S.; Lee, A.; Min, S.; Hong, M. Deep Learning in Multi-Class Lung Diseases’ Classification on Chest X-ray Images. Diagnostics 2022, 12, 915. https://doi.org/10.3390/diagnostics12040915
Arora, R., Rao, G.V.E., Banerjea, S. et al. MLDC: multi-lung disease classification using quantum classifier and artificial neural networks. Neural Comput & Applic (2023). https://doi.org/10.1007/s00521-023-09207-3
Hong M, Rim B, Lee H, Jang H, Oh J, Choi S. Multi-Class Classification of Lung Diseases Using CNN Models. Applied Sciences. 2021; 11(19):9289.
Sami, Azam. (2022). LungNet22: A Fine-Tuned Model for Multiclass Classification and Prediction of Lung Disease Using X-ray Images. Journal of Personalized Medicine, doi: 10.3390/jpm12050680
Sripada, N.K., Mohammed Ismail, B. (2022). A Multi-Class Skin Cancer Classification Through Deep Learning. In: Suma, V., Fernando, X., Du, KL., Wang, H. (eds) Evolutionary Computing and Mobile Sustainable Networks. Lecture Notes on Data Engineering and Communications Technologies, vol 116. Springer, Singapore. https://doi.org/10.1007/978-981-16-9605-3_36
Hassaan, Malik., Tayyaba, Anees., Mui-zzud-Din. (2022). BDCNet: multi-classification convolutional neural network model for classification of COVID-19, pneumonia, and lung cancer from chest radiographs. Multimedia Systems, doi: 10.1007/s00530-021-00878-3
San-li, Yi., Sheng-Lin, Qin., Fu-Rong, She., Tianze, Wang. (2022). RED-CNN: The Multi-Classification Network for Pulmonary Diseases. Electronics, doi: 10.3390/electronics11182896
Ramya, Mohan., A., Rama., Kirupa, Ganapathy. (2022). Comparison of Convolutional Neural Network for Classifying Lung Diseases from Chest CT Images. International Journal of Pattern Recognition and Artificial Intelligence, doi: 10.1142/s0218001422400031
Sripada, N. K., Challa, S. C., & Kothakonda, S. (2023, October). AI-Driven Nutritional Assessment Improving Diets with Machine Learning and Deep Learning for Food Image Classification. In 2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS) (pp. 138-144). IEEE.
Jumin, Zhao., Chen, Zhang., Dengao, Li., Jing, Niu. (2020). Combining multi-scale feature fusion with multi-attribute grading, a CNN model for benign and malignant classification of pulmonary nodules. Journal of Digital Imaging, doi: 10.1007/S10278-020-00333-1
Farhan, A.M.Q., Yang, S. Automatic lung disease classification from the chest X-ray images using hybrid deep learning algorithm. Multimed Tools Appl 82, 38561–38587 (2023). https://doi.org/10.1007/s11042-023-15047-z
Khan, R.H., Miah, J., Arafat, S.Y., Syeed, M.M. and Ca, D.M., 2023, November. Improving Traffic Density Forecasting in Intelligent Transportation Systems Using Gated Graph Neural Networks. In 2023 15th International Conference on Innovations in Information Technology (IIT) (pp. 104-109). IEEE.
İLHAN, H., Elbir, A., SERBES, G. and Aydin, N., 2023. The evaluation of nature-inspired optimization techniques for contrast enhancement in images: A novel software tool. Traitement du Signal, 40(4).
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
