Predicting invasive ductal carcinoma tissues in whole slide images of breast Cancer by using convolutional neural network model and multiple classifiers
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-022-12114-9.pdf
Reference28 articles.
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2. Alzubaidi L, Al-Shamma O, Fadhel MA, Farhan L, Zhang J, Duan Y (2020) Optimizing the performance of breast cancer classification by employing the same domain transfer learning from hybrid deep convolutional neural network model. Electronics 9(3):445
3. Amakdouf H, Zouhri A, El Mallahi M, Tahiri A, Chenouni D, Qjidaa H (2021) Artificial intelligent classification of biomedical color image using quaternion discrete radial Tchebichef moments. Multimed Tools Appl 80(2):3173–3192
4. Bolhasani H, Amjadi E, Tabatabaeian M, Jassbi SJ (2020) A histopathological image dataset for grading breast invasive ductal carcinomas. Inform Med Unlocked 19:100341
5. Cruz-Roa A, Basavanhally A, González F, Gilmore H, Feldman M, Ganesan S, Shih N, Tomaszewski J, Madabhushi A (2014) Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks. In: Medical Imaging 2014: Digital pathology, International Society for Optics and Photonics, 9041: 904103.
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