SECS: An effective CNN joint construction strategy for breast cancer histopathological image classification
Tài liệu tham khảo
Abdullah-Al, 2018, Histopathological breast-image classification using local and frequency domains by convolutional neural network, Information., 9, 19, 10.3390/info9010019
Alex Krizhevsky, Sutskever, I., Hinton, G.E., 2012. ImageNet Classification with Deep Convolutional Neural Networks, NIPS. 1097–91105.
Bardou, 2018, Classification of breast cancer based on histology images using convolutional neural networks, IEEE Access., 6, 24680, 10.1109/ACCESS.2018.2831280
Boumaraf, 2021, A new transfer learning based approach to magnification dependent and independent classification of breast cancer in histopathological images, Biomed. Signal Process Control., 63, 10.1016/j.bspc.2020.102192
Chattopadhyay, 2022, MTRRE-Net: A deep learning model for detection of breast cancer from histopathological images, Comput. Biol. Medi., 150
Chattopadhyay, 2022, DRDA-Net: Dense residual dual-shuffle attention network for breast cancer classification using histopathological images, Comput. Biol. Med., 145, 10.1016/j.compbiomed.2022.105437
Chaudhari, S., Polatkan, G., Ramanath, R., Mithal, V., 2021. An Attentive Survey of Attention Models, ACM Trans. Intell. Syst. Technol. 2, 1. http://doi.org/arXiv.1904.02874.
Demir, 2021, DeepBreastNet: A novel and robust approach for automated breast cancer detection from histopathological images, Biocybern. Biomed. Eng., 41, 1123, 10.1016/j.bbe.2021.07.004
Deng, J., Dong, W., Socher, R., Li, L.J., Kai, L., Li, F.-F., 2009. ImageNet: A large-scale hierarchical image database, In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. http://doi.org/10.1109/CVPR.2009.5206848.
Fan, 2022, COVID-19 CT image recognition algorithm based on transformer and CNN, Displays., 72, 10.1016/j.displa.2022.102150
Fang, 2022, Enhanced task attention with adversarial learning for dynamic multi-task CNN, Pattern Recognit., 128, 10.1016/j.patcog.2022.108672
Faruqui, 2021, LungNet: A hybrid deep-CNN model for lung cancer diagnosis using CT and wearable sensor-based medical IoT data, Comput. Biol. Med., 139, 10.1016/j.compbiomed.2021.104961
Han, 2017, Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model, Sci Rep., 7, 4172, 10.1038/s41598-017-04075-z
Hongping Hu, 2022, Breast cancer histopathological images recognition based on two-stage nuclei segmentation strategy, PLoS One., 17, e0266973, 10.1371/journal.pone.0266973
Hou, Q., Zhou, D., Feng, J., 2021. Coordinate Attention for Efficient Mobile Network Design. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 13708–13717. https://doi.org/10.1109/CVPR46437.2021.01350.
Hu, 2022, Aerodynamic data predictions based on multi-task learning, Appl. Soft. Comput., 116, 10.1016/j.asoc.2021.108369
Huang, G., Liu, Z., Maaten, L.V.D., Weinberger, K.Q., 2017. Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition. 2261-2269. https://doi.org/10.1109/CVPR.2017.243.
Ibraheem, 2021, 3PCNNB-Net: Three Parallel CNN Branches for Breast Cancer Classification Through Histopathological Images, J. Med. Biol. Eng., 41, 494, 10.1007/s40846-021-00620-4
Ioffe, S., Szegedy, C., 2015. Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd International Conference on International Conference on Machine Learning. vol. 37, pp. 448–456. http://doi.org/10.48550/arXiv.150203167.
Karuppasamy, A., Abdesselam, A., Hedjam, R., Zidoum, H., Al-Bahri, M., 2022. Recent CNN-based techniques for breast cancer histology image classification, TJER. 19, 41–53. https://doi.org/10.53540/tjer.vol19iss1pp41-53.
Khatami, 2018, A sequential search-space shrinking using CNN transfer learning and a Radon projection pool for medical image retrieval, Expert Syst. Appl., 100, 224, 10.1016/j.eswa.2018.01.056
Kumar, 2020, Deep feature learning for histopathological image classification of canine mammary tumors and human breast cancer, Inf. Sci., 508, 405, 10.1016/j.ins.2019.08.072
Kumar, 2021, MobiHisNet: A Lightweight CNN in Mobile Edge Computing for Histopathological Image Classification, IEEE Internet Things J., 8, 17778, 10.1109/JIOT.2021.3119520
Lin, 2018, Focal loss for dense object detection, IEEE T Pattern Anal., 42, 2999
Matos, J.d., Britto, A.d.S., Oliveira, L.E.S., Koerich, A.L., 2019. Double transfer learning for breast cancer histopathologic image classification. In: 2019 International Joint Conference on Neural Networks, pp. 1–8, http://doi.org/10.1109/IJCNN.2019.8852092.
Niu, 2021, A review on the attention mechanism of deep learning, Neurocomputing., 452, 48, 10.1016/j.neucom.2021.03.091
Nomani, 2022, PSOWNNs-CNN: A Computational Radiology for Breast Cancer Diagnosis Improvement Based on Image Processing Using Machine Learning Methods, Comput. Intell. Neurosci., 2022, 5667264, 10.1155/2022/5667264
Pan, 2010, A survey on transfer learning, IEEE Trans Knowl Data Eng., 22, 1345, 10.1109/TKDE.2009.191
Reinert, 2021, Perspectives on the systemic staging in newly diagnosed breast cancer, Clin. Breast Cancer., 21, 309, 10.1016/j.clbc.2021.03.010
Selvaraju, 2020, Grad-CAM: visual explanations from deep networks via gradient-based localization, Int. J. Comput. Vis., 128, 336, 10.1007/s11263-019-01228-7
Senousy, 2022, MCUa: multi-level context and uncertainty aware dynamic deep ensemble for breast cancer histology image classification, IEEE T Bio-Med. Eng., 69, 818, 10.1109/TBME.2021.3107446
Shallu, 2018, Breast cancer histology images classification: Training from scratch or transfer learning?, ICT Express., 4, 247, 10.1016/j.icte.2018.10.007
Shi, 2020, No-reference stereoscopic image quality assessment using a multi-task CNN and registered distortion representation, Pattern Recognit., 100, 10.1016/j.patcog.2019.107168
Shokraei Fard, 2022, From CNNs to GANs for cross-modality medical image estimation, Comput. Biol. Med., 146, 10.1016/j.compbiomed.2022.105556
Spanhol, F.A., Oliveira, L.S., Petitjean, C., Heutte, L.J.I.T.o.B.E., 2016. A Dataset for Breast Cancer Histopathological Image Classification, IEEE T Bio-Med. Eng. 63, 1455–1462. https://doi.org/10.1109/TBME.2015.2496264.
Spanhol, F.A., Oliveira, L.S., Petitjean, C., Heutte. L., 2016. Breast cancer histopathological image classification using Convolutional Neural Networks. In: 2016 International Joint Conference on Neural Networks, pp. 2560–2567, http://doi.org/10.1109/IJCNN.2016.7727519.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.J.J.o.M.L.R., 2014. Dropout: A Simple Way to Prevent Neural Networks from Overfitting, J Mach Learn Res, 15, 1929–1958. https://dl.acm.org/doi/10.5555/2627435.2670313.
Thawkar, 2022, Feature selection and classification in mammography using hybrid crow search algorithm with Harris hawks optimization, Biocybern. Biomed. Eng., 42, 1094, 10.1016/j.bbe.2022.09.001
Trapani, 2022, Global challenges and policy solutions in breast cancer control, Cancer Treat. Rev., 104, 10.1016/j.ctrv.2022.102339
Tsafas, 2022, Application of a deep-learning technique to non-linear images from human tissue biopsies for shedding new light on breast cancer diagnosis, IEEE J. Biomed. Health Inform., 26, 1188, 10.1109/JBHI.2021.3104002
Usama, 2019, REMOVED: Equipping recurrent neural network with CNN-style attention mechanisms for sentiment analysis of network reviews, Comput. Commun., 148, 98, 10.1016/j.comcom.2019.08.002
Vankdothu, 2022, A Brain Tumor Identification and Classification Using Deep Learning based on CNN-LSTM Method, Comput. Electr. Eng., 101, 10.1016/j.compeleceng.2022.107960
Wong, 2020, Multi-task CNN for restoring corrupted fingerprint images1, Pattern Recognit., 101, 10.1016/j.patcog.2020.107203
Xiang, Z., Ting, Z., Weiyan, F., Cong, L., 2018. Breast Cancer Diagnosis from Histopathological Image based on Deep Learning. In: The 31st China Control and Decision-making Conference Nanchang, Jiangxi, vol. 6, pp. 24680. https://doi.org/10.1109/ACCESS.2018.2831280.
Yao, 2020, Speech emotion recognition using fusion of three multi-task learning-based classifiers: HSF-DNN, MS-CNN and LLD-RNN, Speech Commun., 120, 11, 10.1016/j.specom.2020.03.005
Zaalouk, 2022, A Deep learning computer-aided diagnosis approach for breast cancer, Bioengineering., 9, 391, 10.3390/bioengineering9080391