Comparative study of deep learning models on the images of biopsy specimens for diagnosis of lung cancer treatment

Liu Liu1, Cong Li2
1Department of Oncology, Fuxin Central Hospital, Fuxin, 123000, China
2School of Computer Science and Technology, Central South University, Changsha, 410083, China

Tài liệu tham khảo

Amin, 2014, Classification of breast tumor using electrical impedance and machine learning techniques [J], Physiological Measurement, 35, 965, 10.1088/0967-3334/35/6/965 Bach, 2007, Screening for lung cancer: ACCP evidence-based clinical practice guidelines (2nd edition), Chest, 132, 69S, 10.1378/chest.07-1349 Bray, 2018, Global cancer statistics 2018: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries, J] Ca Cancer J Clin, 68, 394, 10.3322/caac.21492 Cao, 2021, Changing profiles of cancer burden worldwide and in China: A secondary analysis of the global cancer statistics 2020, Chinese Medical Journal, 134, 783, 10.1097/CM9.0000000000001474 Ciresan, 2011, Flexible, high-performance convolutional neural networks for image classification [A], 1237 Hamet, 2017, Artificial intelligence in medicine, Metabolism Clinical and Experimental, 69S, S36, 10.1016/j.metabol.2017.01.011 He, 2016, Deep residual learning for image recognition [A], 770 Hosny, 2018, Artificial intelligence in radiology, Nature Reviews Cancer, 18, 500, 10.1038/s41568-018-0016-5 Ioffe, 2015, Batch normalization: Accelerating deep network training by reducing internal covariate shift [A] Jaworek-Korjakowska, 2016, Automatic classification of specific melanocytic lesions using artificial intelligence [J/OL], BioMed Research International, 10.1155/2016/8934242 Krizhevsky, 2017, ImageNet Classification with deep convolutional neural networks [J], Communications of the ACM, 60, 84, 10.1145/3065386 Li, 2018, Deep learning-based gastric cancer identification [A], 182 Litjens, 2017, A survey on deep learning in medical image analysis [J], Medical Image Analysis, 42, 60, 10.1016/j.media.2017.07.005 Russakovsky, 2015, Imagenet large scale visual recognition challenge [J], International Journal of Computer Vision, 1 15, 21 1, 10.1007/s11263-015-0816-y Srivastava, 2014, Dropout: A simple way to prevent neural networks from overfitting [J], Journal of Machine Learning Research, 15, 1929 Tang, 2022, Auto-encoder-extreme learning machine model for boiler NOx emission concentration prediction, Energy, 256, 10.1016/j.energy.2022.124552 Tripathy, 2014, Artificial Intellintelligence-based classification of breast cancer using cellular images [J], RSC Advances, 4, 9349, 10.1039/c3ra47489e Wild, 2020 Zhang, 2018, A predictive model for distinguishing radiation necrosis from tumour progression after gamma knife radiosurgery based on radiomic features from MR images, European Radiology, 28, 2255, 10.1007/s00330-017-5154-8 Zhao, 2023, CT synthesis from MR in the pelvic area using Residual Transformer Conditional GAN, Computerized Medical Imaging and Graphics, 103, 10.1016/j.compmedimag.2022.102150 Zhao, 2022, Geometrical deviation modeling and monitoring of 3D surface based on multi-output Gaussian process, Measurement, 199, 10.1016/j.measurement.2022.111569 Zhou, 2022, Classification of precancerous lesions based on fusion of multiple hierarchical features, Computer Methods and Programs in Biomedicine, 229