Advanced Lung Disease Detection and Classification Using Ge-U-Net-ODLwith Gabor Filters and Entropy-Based Feature Selection
DOI:
https://doi.org/10.69996/jsihs.2024011Keywords:
Lung X-ray images, Deep learning, Feature extraction, Entropy, Gabor filters, Automated diagnosisAbstract
Lung X-ray images play a crucial role in the detection and diagnosis of various lung diseases,including pneumonia, tuberculosis, and lung cancer. These images provide a non-invasive method for visualizing lung structures, allowing radiologists and machine learning models to identify abnormalities such as nodules, masses, or fluid accumulation. With the advancement of deep learning techniques, lung X-ray images are now used in automated systems that can detect and classify diseases with high accuracy. By applying sophisticated algorithms like GE-U-Net-ODL, Gabor filters, and entropy-based feature extraction, these images are analyzed pixel by pixel to enhance feature representation and improve diagnostic precision. This paper presents a novel approach for lung disease detection and classification using the GE-U-Net-ODL model, which integrates advanced preprocessing techniques and deep learning architectures. The study leverages the NIH Chest X-ray dataset and employs a variety of feature extraction and selection methods, including Gabor filters, entropy-based techniques, and multi-scale inputs. A detailed comparative analysis of different model configurations demonstrates that the GE-U-Net-ODL model with Transfer Learning achieves the highest classification accuracy of 95.0%, alongside superior precision (93.5%), recall (94.0%), and F1-score (93.7%). Other configurations, such as those utilizing data augmentation and hybrid filters, also showed notable performance improvements. The research underscores the model's effectiveness in enhancing diagnostic accuracy for lung diseases while balancing training and inference times
References
[1] S. Goyal and R. Singh, “Detection and classification of lung diseases for pneumonia and Covid-19 using machine and deep learning technique,” Journal of Ambient Intelligence and Humanized Computing, vol.14, no.4, pp.3239-3259, 2023.
[2] A. Kabiraj, T. Meena, P.B. Reddy and S. Roy, “Detection and classification of lung disease using deep learning architecture from x-ray images,” In International Symposium on visual computing, pp. 444-455, 2022.
[3] S. Ashwini, J.R. Arunkumar, R.T. Prabu, N.H. Singh and N.P. Singh, “Diagnosis and multiclassification of lung diseases in CXR images using optimized deep convolutional neural network,”Soft Computing, vol.28, no.7, pp.6219-6233, 2024.
[4] S.H. Karaddi and L.D. Sharma, “Automated multi-class classification of lung diseases from CXRimages using pre-trained convolutional neural networks,” Expert Systems with Applications, vol.211,pp.118650, 2023.
[5] 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 Engineering Journal, vol.64, pp.923-935, 2023.
[6] R. Pandian, V. Vedanarayanan, D.R. Kumar and R. Rajakumar, “Detection and classification of lung cancer using CNN and Google net,” Measurement: Sensors, vol.24, pp.100588, 2022.
[7] S. Kim, B. Rim, S. Choi, A. Lee, S. Min and M. Hong, “Deep learning in multi-class lung diseases’ classification on chest X-ray images,” Diagnostics, vol.12, no.4, pp.915, 2022.
[8] M. Soni, S. Gomathi, P. Kumar, P.P. Churi, M.A. Mohammed and A.O. Salman, “Hybridizing convolutional neural network for classification of lung diseases,” International Journal of Swarm Intelligence Research (IJSIR), vol.13. no.2, pp.1-15, 2022.
[9] Q. M. Zarandah, S. M. Daud and S.S. Abu-Naser, “A Systematic Literature Review Of Machine and Deep Learning-Based Detection And Classification Methods for Diseases Related To the Respiratory System,” Journal of Theoretical and Applied Information Technology, vol.101, no.4, pp.1273-1296,2023.
[10] Q. M. Zarandah, S. M. Daud and S.S. Abu-Naser, “A Systematic Literature Review Of Machine and Deep Learning-Based Detection And Classification Methods for Diseases Related To the Respiratory System,” Journal of Theoretical and Applied Information Technology, vol.101, no.4, pp.1273-1296,2023.
[11] F. J. M. Shamrat, S. Azam, A. Karim, R. Islam, Z. Tasnim et al., “LungNet22: a fine-tuned model for multiclass classification and prediction of lung disease using X-ray images,” Journal of Personalized Medicine, vol.12, no.5, pp.680, 2022.
[12] R. K. P. M. T. K. R. Rajagopal, R. Karthick, P. Meenalochini and T. Kalaichelvi, “Deep Convolutional Spiking Neural Network optimized with Arithmetic optimization algorithm for lung disease detection using chest X-ray images,” Biomedical Signal Processing and Control, vol.79, pp.104197, 2023.
[13] F. J. M. Shamrat, S. Azam, A. Karim, K. Ahmed, F.M. Bui et al., “High-precision multiclass classification of lung disease through customized MobileNetV2 from chest X-ray images,” Computers in Biology and Medicine, vol.155, no.106646, 2023.
[14] C. M. Sharma, L. Goyal, V. M. Chariar and N. Sharma, “Lung Disease Classification in CXR Images Using Hybrid Inception‐ResNet‐v2 Model and Edge Computing,” Journal of Healthcare Engineering, vol.2022, no.1, pp.9036457, 2022.
[15] V. Ravi, V. Acharya and M. Alazab, “A multichannel EfficientNet deep learning-based stacking ensemble approach for lung disease detection using chest X-ray images,” Cluster Computing, vol.26, no.2, pp.1181-1203, 2023.
[16] Y. H. Bhosale and K.S. Patnaik, “PulDi-COVID: Chronic obstructive pulmonary (lung) diseases with COVID-19 classification using ensemble deep convolutional neural network from chest X-ray images to minimize severity and mortality rates,” Biomedical Signal Processing and Control, vol.81, pp.104445, 2023.
[17] E. A. Siddiqui, V. Chaurasia and M. Shandilya, “Detection and classification of lung cancer computed tomography images using a novel improved deep belief network with Gabor filters,” Chemometrics and Intelligent Laboratory Systems, vol.235, pp.104763, 2023.
[18] M. Nawaz, T. Nazir, J. Baili, M.A. Khan, Y.J. Kim and J.H. Cha, “CXray-EffDet: chest disease detection and classification from X-ray images using the EfficientDet model,” Diagnostics, vol.13, no.2, pp.248, 2023.
[19] B. AR, V.K. RS and K. SS, “LCD-capsule network for the detection and classification of lung cancer on computed tomography images,” Multimedia Tools and Applications, vol.82, no.24, pp.37573- 37592, 2023.
[20] M. Humayun, R. Sujatha, S.N. Almuayqil and N.Z. Jhanjhi, “A transfer learning approach with a convolutional neural network for the classification of lung carcinoma,” In Healthcare, vol. 10, no. 6, pp. 1058, 2022.
[21] Z. Xu, H. Ren, W. Zhou and Z. Liu, “ISANET: Non-small cell lung cancer classification and detection based on CNN and attention mechanism,” Biomedical Signal Processing and Control, vol.77, pp.103773, 2022.
[22] M. Fraiwan, L. Fraiwan, M. Alkhodari and O. Hassanin, “Recognition of pulmonary diseases from lung sounds using convolutional neural networks and long short-term memory,” Journal of Ambient Intelligence and Humanized Computing, pp.1-13, 2022.
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