... Research on multi - load remote wireless power transfer system . Power Electron . 55 ( 09 ) , 139–142 ( 2021 ) 2. Zhang , W. , Dong , Q .: Fuzzy association rules mining method based on GSO optimization MF in uncertainty data . Appl ...
"This paper deals with the automation of the detection of the cervical cancer through histology images.
... Classification with Local Binary Patterns. IEEE Trans Pattern Anal Mach Intell (2002) 24(7):971. doi: 10.1007/3-540-44732-6_41 33. Guo Z, Zhang L, Zhang D. A Completed Modeling of Local ... Cancer Histopathological Image Recognition.
... classification problem domains with very large-scale datasets like ImageNet (Krizhevsky, Sutskever, & Hinton, 2012), face recognition (Parkhi, Vedaldi ... images are. 51 A Hybrid Deep Learning and Handcrafted Feature Approach.
... Deep learning for cell image segmentation and ranking. Comput. Med. Imaging Graph. 72, 13–21 (2019) 21. Toratani, M ... Cancer Res. 78(23), 6703–6707 (2018) 22. Saslow, D., et al.: American cancer society, American society for ...
This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component are applied to medical image detection, ...
... learning algorithms for pattern classification. Int. J. Adv. Res. Artif. Intell. 2, 34–38 (2013). https://doi.org/10.14569/ ijarai.2013.020206 2. Sebai, M., Wang, T., Al-Fadhli, S.A.: PartMitosis: a partially supervised deep learning ...
... Cancer diagnosis in histopathological image: CNN based approach. Inf. Med. Unlocked 16, 100231 (2019) 22. Ahmed, Z., Mohamed, K., Zeeshan, S., Dong, X.: Artificial intelligence with multi-functional machine learning platform development ...
... nuclei segmentation has been done with a publicly available dataset i.e. 2018 Data Science Bowl (Kaggle, n.d.). Literature Review benchmark models ... Study and Analysis of Deep Neural Networks for Cancer Using Histopathology Images.
Quantitative analysis of histopathology images is important for both clinical purposes (e.g. to reduce/eliminate inter- and intra-observer variations in diagnosis) and research purposes (e.g. to understand the biological mechanisms of the ...