Recognition and Classification of Childhood Pneumonia Based on Improved Inception-ResNet-v2

Junhao Yang; Zhiqing Ma; Guohui Wei; Shuang Zhao

doi:10.3788/LOP221774

[1] Tandon Y K, Bartholmai B J, Koo C W. Putting artificial intelligence (AI) on the spot: machine learning evaluation of pulmonary nodules[J]. Journal of Thoracic Disease, 12, 6954-6965(2020).

[2] Taraseviciene-Stewart L, Voelkel N F. Molecular pathogenesis of emphysema[J]. The Journal of Clinical Investigation, 118, 394-402(2008).

[3] Hashmi M F, Katiyar S, Keskar A G et al. Efficient pneumonia detection in chest X-ray images using deep transfer learning[J]. Diagnostics, 10, 417(2020).

[5] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[C], 1097-1105(2012).

[6] Saraiva A A, Santos D B S, Costa N J C et al. Models of learning to classify X-ray images for the detection of pneumonia using neural networks[C](2019).

[7] Rajpurkar P, Irvin J, Zhu K et al. CheXNet: radiologist-level pneumonia detection on chest X-rays with deep learning[EB/OL]. https://arxiv.org/abs/1711.05225

[8] Wang X S, Peng Y F, Lu L et al. ChestX-Ray8: hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases[C], 3462-3471(2017).

[9] Ayan E, Ünver H M. Diagnosis of pneumonia from chest X-ray images using deep learning[C](2019).

[10] Chouhan V, Singh S K, Khamparia A et al. A novel transfer learning based approach for pneumonia detection in chest X-ray images[J]. Applied Sciences, 10, 559(2020).

[11] Barhoom A M A, Abu-Naser S S. Diagnosis of pneumonia using deep learning[J]. International Journal of Academic Engineering Research (IJAER), 6, 48-68(2022).

[12] Acharya A K, Satapathy R. A deep learning-based approach towards the automatic diagnosis of pneumonia from chest radio-graphs[J]. Biomedical and Pharmacology Journal, 13, 449-455(2020).

[13] Rahman T, Chowdhury M E H, Khandakar A et al. Transfer learning with deep convolutional neural network (CNN) for pneumonia detection using chest X-ray[J]. Applied Sciences, 10, 3233(2020).

[14] Szegedy C, Ioffe S, Vanhoucke V et al. Inception-v4, inception-ResNet and the impact of residual connections on learning[C], 4278-4284(2017).

[15] Dai Y M, Gieseke F, Oehmcke S et al. Attentional feature fusion[C], 3559-3568(2021).

[16] Chen S S, Chen M H, Ma W F. Research on automatic classification of optical coherence tomography retina image based on multi-channel[J]. Chinese Journal of Lasers, 48, 2307001(2021).

[17] Zhang X D, Wang T J, Zhu S J et al. Hyperspectral image classification based on dilated convolutional attention neural network[J]. Acta Optica Sinica, 41, 0310001(2021).

[18] Gu Y, Lu X Q, Zhang B H et al. Automatic lung nodule detection using multi-scale dot nodule-enhancement filter and weighted support vector machines in chest computed tomography[J]. PLoS One, 14, e0210551(2019).

[19] Szegedy C, Liu W, Jia Y Q et al. Going deeper with convolutions[C](2015).

[20] Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift[C], 448-456(2015).

[21] Szegedy C, Vanhoucke V, Ioffe S et al. Rethinking the inception architecture for computer vision[C], 2818-2826(2016).

[22] Xu B, Wang N Y, Chen T Q et al. Empirical evaluation of rectified activations in the convolutional network[EB/OL]. https://arxiv.org/abs/1505.00853

[23] Kermany D S, Goldbaum M, Cai W J et al. Identifying medical diagnoses and treatable diseases by image-based deep learning[J]. Cell, 172, 1122-1131(2018).

[24] Wang Z H, Kang H, Chen DF et al. Lightweight deep learning network assisted cell classification using lensless computational microscopic imaging data[J]. Chinese Journal of Lasers, 49, 0507204(2022).

[25] Newey W K. Adaptive estimation of regression models via moment restrictions[J]. Journal of Econometrics, 38, 301-339(1988).

[26] Deng Q, Lei Y J, Tian F. Optimized convolutional neural network method for classification of pneumonia images[J]. Journal of Computer Applications, 40, 71-76(2020).

[27] He X Y, Zhang X L. Pneumonia image recognition model based on the deep neural network[J]. Journal of Computer Applications, 39, 1680-1684(2019).

[28] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[EB/OL]. https://arxiv.org/abs/1409.1556

[29] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

[30] Hartono A P, Luhur C R, Indriyani C A et al. Evaluating deep learning for CT scan COVID-19 automatic detection[C](2021).

[31] Aytaç U C, Güneş A, Ajlouni N. A novel adaptive momentum method for medical image classification using convolutional neural network[J]. BMC Medical Imaging, 22, 34(2022).