Knaul, Felicia Marie, ed. Closing the cancer divide: an equity imperative. Vol. 2. Harvard university press, 2012.

Chollet, François. "Xception: Deep learning with depthwise separable convolutions." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 1251–1258.

Kang, Qi, et al. "A distance-based weighted undersampling scheme for support vector machines and its application to imbalanced classification." IEEE transactions on neural networks and learning systems 29.9 (2017): 4152–4165.

Yue, Wenbin, et al. "Machine learning with applications in breast cancer diagnosis and prognosis." Designs 2.2 (2018): 13.

Belgiu, Mariana, and Lucian Drăguţ. "Random forest in remote sensing: A review of applications and future directions." ISPRS journal of photogrammetry and remote sensing 114 (2016): 24-31.

Article  ADS  Google Scholar 

LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." nature 521.7553 (2015): 436–444.

Gandomkar, Ziba, Patrick C. Brennan, and Claudia Mello-Thoms. "MuDeRN: Multi-category classification of breast histopathological image using deep residual networks." Artificial intelligence in medicine 88 (2018): 14-24.

Article  PubMed  Google Scholar 

Alkassar, S., et al. "Going deeper: magnification‐invariant approach for breast cancer classification using histopathological images." IET Computer Vision 15.2 (2021): 151-164.

Article  Google Scholar 

George, Kalpana, et al. "Deep learned nucleus features for breast cancer histopathological image analysis based on belief theoretical classifier fusion." TENCON 2019–2019 IEEE Region 10 Conference (TENCON). IEEE, 2019: 344–349.

Huang, Pan, et al. "A ViT-AMC network with adaptive model fusion and multiobjective optimization for interpretable laryngeal tumor grading from histopathological images." IEEE Transactions on Medical Imaging 42.1 (2022): 15–28.

Luo, Jiayang, et al. "DCA-DAFFNet: An End-to-end Network with Deformable Fusion Attention and Deep Adaptive Feature Fusion for Laryngeal Tumor Grading from Histopathology Images." IEEE Transactions on Instrumentation and Measurement (2023).

Pan, Huang, et al. "Breast tumor grading network based on adaptive fusion and microscopic imaging." Opto-Electronic Engineering 50.1 (2023): 220158–1.

Huang, Pan, et al. "FABNet: fusion attention block and transfer learning for laryngeal cancer tumor grading in P63 IHC histopathology images." IEEE Journal of Biomedical and Health Informatics 26.4 (2021): 1696–1707.

Huang, Pan, et al. "Interpretable laryngeal tumor grading of histopathological images via depth domain adaptive network with integration gradient CAM and priori experience-guided attention." Computers in Biology and Medicine 154 (2023): 106447.

Kumar, Yogesh, and Manish Mahajan. "Intelligent behavior of fog computing with IOT for healthcare system." International Journal of Scientific & Technology Research 8.7 (2019): 674-679.

Google Scholar 

Razi, Saeid, et al. "The incidence and mortality of ovarian cancer and their relationship with the Human Development Index in Asia." ecancermedicalscience 10 (2016).

Song, Yan-Yan, and L. U. Ying. "Decision tree methods: applications for classification and prediction." Shanghai archives of psychiatry 27.2 (2015): 130.

Witten, Ian H., and Eibe Frank. "Data mining: practical machine learning tools and techniques with Java implementations." Acm Sigmod Record 31.1 (2002): 76-77.

Article  Google Scholar 

Quinlan, J. Ross. "Induction of decision trees." Machine learning 1 (1986): 81–106.

Amato, Filippo, et al. "Artificial neural networks in medical diagnosis." Journal of applied biomedicine 11.2 (2013): 47–58.

Cortes, Corinna, and Vladimir Vapnik. "Support-vector networks." Machine learning 20 (1995): 273-297.

Article  Google Scholar 

Ho, Tin Kam. "The random subspace method for constructing decision forests." IEEE transactions on pattern analysis and machine intelligence 20.8 (1998): 832-844.

Article  Google Scholar 

Bhatia, Vandana, and Rinkle Rani. "Dfuzzy: a deep learning-based fuzzy clustering model for large graphs." Knowledge and Information Systems 57 (2018): 159-181.

Article  Google Scholar 

Bayramoglu, Neslihan, Juho Kannala, and Janne Heikkilä. "Deep learning for magnification independent breast cancer histopathology image classification." 2016 23rd International conference on pattern recognition (ICPR). IEEE, 2016: 2440-2445

Google Scholar 

Yu, Cuiru, et al. "Breast cancer classification in pathological images based on hybrid features." Multimedia Tools and Applications 78 (2019): 21325–21345.

Vo, Duc My, Ngoc-Quang Nguyen, and Sang-Woong Lee. "Classification of breast cancer histology images using incremental boosting convolution networks." Information Sciences 482 (2019): 123–138.

Saber, Abeer, et al. "A novel deep-learning model for automatic detection and classification of breast cancer using the transfer-learning technique." IEEE Access 9 (2021): 71194–71209.

Teng, Jing, et al. "A Dynamic Bayesian Model for Breast Cancer Survival Prediction." IEEE Journal of Biomedical and Health Informatics 26.11 (2022): 5716–5727.

Eren, Furkan, et al. "DeepCAN: A Modular Deep Learning System for Automated Cell Counting and Viability Analysis." IEEE journal of biomedical and health informatics 26.11 (2022): 5575–5583.

Lu, Si-Yuan, Shui-Hua Wang, and Yu-Dong Zhang. "SAFNet: A deep spatial attention network with classifier fusion for breast cancer detection." Computers in Biology and Medicine 148 (2022): 105812.

Article  CAS  PubMed  Google Scholar 

Dey, Soumyajyoti, et al. "GC-EnC: A Copula based ensemble of CNNs for malignancy identification in breast histopathology and cytology images." Computers in Biology and Medicine 152 (2023): 106329.

Karunakar, Yogesh, Alhad Kuwadekar, and K. Narayanan. "A Windows Mobile Based Application for Detection of Cancer in Squamous Cell." 2010 Fourth International Conference on Next Generation Mobile Applications, Services and Technologies. IEEE, 2010: 19–24.

Ghaderi, M., et al. "mHealth platform for breast cancer risk assessment." 2015 international conference on healthcare informatics. IEEE, 2015:1-14.

Google Scholar 

Patil, Rajeshwari S., and Nagashettappa Biradar. "Automated mammogram breast cancer detection using the optimized combination of convolutional and recurrent neural network." Evolutionary intelligence 14 (2021): 1459-1474.

Article  Google Scholar 

Shrivastav, Kumar Dron, et al. "Earth mover’s distance-based tool for rapid screening of cervical cancer using cervigrams." Applied Sciences 12.9 (2022): 4661.

Sandler, Mark, et al. "Mobilenetv2: Inverted residuals and linear bottlenecks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 4510–4520.

Howard, Andrew G., et al. "Mobilenets: Efficient convolutional neural networks for mobile vision applications." arXiv preprint arXiv:1704.04861(2017): 1–9.

Chen, Guangyong, et al. "Rethinking the usage of batch normalization and dropout in the training of deep neural networks." arXiv preprint arXiv:1905.05928(2019): 1–10.

Toğaçar, Mesut, et al. "BreastNet: A novel convolutional neural network model through histopathological images for the diagnosis of breast cancer." Physica A: Statistical Mechanics and its Applications 545 (2020): 123592.

Toğaçar, Mesut, Burhan Ergen, and Zafer Cömert. "Application of breast cancer diagnosis based on a combination of convolutional neural networks, ridge regression and linear discriminant analysis using invasive breast cancer images processed with autoencoders." Medical hypotheses 135 (2020): 109503.

Article  PubMed  Google Scholar 

Zhang, Xiangyu, et al. "Shufflenet: An extremely efficient convolutional neural network for mobile devices." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 6848–6856.