Deep Learning Technique for The Classification of Lung Diseases from X-ray Images
DOI:
https://doi.org/10.29304/jqcsm.2024.16.21544Keywords:
Lung diseases, CNN, VGG , Transfer learningAbstract
Pictures obtained from a chest X-ray can assist in diagnosing and screening various illnesses, including pneumonia, mass, lung opacity, TB, and nodules. The convolutional neural network (CNN) has been devised to predict pulmonary disease. In this investigation, we classify lung disease based on pictures from chest X-rays using a technique known as transfer learning. This method was chosen because it allows us to make more accurate diagnoses. Transfer learning has proven effective for detecting diverse anomalies in limited medical imaging datasets, leading to notable outcomes. This study involves utilizing a dataset comprising a collection of chest X-ray photos obtained from the Kaggle website, including nine different lung diseases (COVID, mass, effusion, lung opacity, nodule, pneumonia, pneumothorax, pulmonary fibrosis, and tuberculosis), as well as images of individuals without any lung ailments. The objective is to employ several transfer learning models (VGG19, Inception V3, Efficient Net V2m, Xception, Mobile Netv3, and Dense net201) to train these images, classify them, and then select the most influential Model for addressing this task. Most models yielded promising results, particularly dense net201, mobile net, and VGG19, which achieved more effective yields with 95.49%, 94.89%, and 93.69% accuracy rates, respectively.
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Kim, S., et al., Deep learning in multi-class lung diseases’ classification on chest X-ray images. Diagnostics, 2022. 12(4): p. 915.
Kandel, I. and M. Castelli, Transfer learning with convolutional neural networks for diabetic retinopathy image classification. A review. Applied Sciences, 2020. 10(6): p. 2021.
Weimann, K. and T.O. Conrad, Transfer learning for ECG classification. Scientific reports, 2021. 11(1): p. 5251.
Wang, C., et al., Pulmonary image classification based on inception-v3 transfer learning model. IEEE Access, 2019. 7: p. 146533-146541.
Liu, J. and Y. Huang, Comparison of different CNN models in tuberculosis detecting. KSII Transactions on Internet and Information Systems (TIIS), 2020. 14(8): p. 3519-3533.
Apostolopoulos, I.D. and T.A. Mpesiana, Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Physical and engineering sciences in medicine, 2020. 43: p. 635-640.
Jain, R., et al., Pneumonia detection in chest X-ray images using convolutional neural networks and transfer learning. Measurement, 2020. 165: p. 108046.
Hong, M., et al., Multi-class classification of lung diseases using CNN models. Applied Sciences, 2021. 11(19): p. 9289.
Yosinski, J., et al., How transferable are features in deep neural networks? Advances in neural information processing systems, 2014. 27.
Kornblith, S., J. Shlens, and Q.V. Le. Do better imagenet models transfer better? in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019.
Shin, H.-C., et al., Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging, 2016. 35(5): p. 1285-1298.
Ikechukwu, A.V., et al., ResNet-50 vs VGG-19 vs training from scratch: A comparative analysis of the segmentation and classification of Pneumonia from chest X-ray images. Global Transitions Proceedings, 2021. 2(2): p. 375-381.
Huang, G., et al. Densely connected convolutional networks. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
Singhal, G., Introduction to densenet with tensorflow. Pluralsight. com.[Online]. Available: https://www. pluralsight. com/guides/introduction-to-densenet-with-tensorflow.[Accessed: 02-Dec, 2020.
Howard, A., et al. Searching for mobilenetv3. in Proceedings of the IEEE/CVF international conference on computer vision. 2019.
Salim, F., et al., DenseNet-201 and Xception pre-trained deep learning models for fruit recognition. Electronics, 2023. 12(14): p. 3132.
Demir, A., F. Yilmaz, and O. Kose. Early detection of skin cancer using deep learning architectures: resnet-101 and inception-v3. in 2019 medical technologies congress (TIPTEKNO). 2019. IEEE.
Szegedy, C., et al., Going deeper with convolutions in: Proceedings of the ieee conference on computer vision and pattern recognition; 2015. Google Scholar, 2015: p. 1-9.
Alshehri, A., et al., Deep attention neural network for multi-label classification in unmanned aerial vehicle imagery. IEEE Access, 2019. 7: p. 119873-119880.
Tummala, S., et al., EfficientNetV2 based ensemble model for quality estimation of diabetic retinopathy images from DeepDRiD. Diagnostics, 2023. 13(4): p. 622.
Chollet, F. Xception: Deep learning with depthwise separable convolutions. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
He, K., et al. Deep residual learning for image recognition. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
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Copyright (c) 2024 Ruaa N. Sadoon, Adala M. Chaid
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