Abstract
Abstract
Pneumonia is a life-threatening disease and early detection can save lives, many automated systems have contributed to the detection of this disease and currently deep learning models have become one of the most widely used models for building these systems. In this study, two deep learning models are combined: DenseNet169 and pre-activation ResNet models and used for automatic detection of pneumonia. DenseNet169 model is an extension of the ResNet model, while the second is a modified version the ResNet model, these models achieved good results in the field of medical imaging. Two methods are used to deal with the problem of unbalanced data: class weight, which enables to control the percentage of data to be used from the original data for each class of data, while the other method is resampling, in which modified images are produced with an equal distribution using data augmentation. The performance of the proposed model is evaluated using a balanced dataset consists of 5856 images. Achieved results were promising compared to several previous studies. The model achieved a precision value of 98%, an area under curve (AUC) based on ROC of 97%, and a loss value of 0.23.
Publisher
Research Square Platform LLC
Reference44 articles.
1. A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images;Chouhan V;Applied Sciences,2020
2. Sharma, A, Negi, M, Goyal, A, Jain, R, Nagrath, P (2021). Detection of pneumonia using ML & DL in python. IOP Conference Series: Materials Science and Engineering, 1022, 1st International Conference on Computational Research and Data Analytics (ICCRDA 2020) 24th October 2020, Rajpura, India. https://doi.org/10.1088/1757-899X/1022/1/012066.
3. Saul, CJ, Urey, DY, Taktakoglu, CD (2019). Early Diagnosis of Pneumonia with Deep Learning. https://doi.org/10.48550/arXiv.1904.00937.
4. Sarkar, R, Hazra, A, Sadhu, K, Ghosh, P (2020). A Novel Method for Pneumonia Diagnosis from Chest X-Ray Images Using Deep Residual Learning with Separable Convolutional Networks. In: Gupta, M., Konar, D., Bhattacharyya, S., Biswas, S. (eds) Computer Vision and Machine Intelligence in Medical Image Analysis. Advances in Intelligent Systems and Computing, 992. Springer, Singapore. https://doi.org/10.1007/978-981-13-8798-2_1.
5. Tilve, A, Nayak, S, Vernekar, S, Turi, D, Shetgaonkar, PR, Aswale, S. (2020). Pneumonia Detection Using Deep Learning Approaches. International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), pp. 1–8, http://doi.org/10.1109/ic-ETITE47903.2020.152.
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