New design strategies of deep heterogenous convolutional neural networks ensembles for breast cancer diagnosis
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/s11042-023-18002-0.pdf
Reference47 articles.
1. Alaoui OE, Zerouaoui H, Idri A (2022) Deep stacked ensemble for breast cancer diagnosis. In: Rocha A, Adeli H, Dzemyda G, Moreira F (eds) Information systems and technologies. WorldCIST 2022. lecture notes in networks and systems vol 468. Springer, Cham. https://doi.org/10.1007/978-3-031-04826-5_44
2. Alom MZ, Yakopcic C, Nasrin MS, Taha TM, Asari VK (2019) Breast Cancer Classification from histopathological images with inception recurrent residual convolutional neural network. J Digit Imaging 32(4):605–617. https://doi.org/10.1007/s10278-019-00182-7
3. Aswathy MA, Jagannath M (2017) Detection of breast cancer on digital histopathology images: Present status and future possibilities. Inform Med Unlocked 8(October 2016):74–79. https://doi.org/10.1016/j.imu.2016.11.001
4. de Oliveira CI, do Nascimento MZ, Roberto GF, Tosta TAA, Martins AS, Neves LA (2023) Hybrid models for classifying histological images: An association of deep features by transfer learning with ensemble classifier. Multimedia Tools Appl. https://doi.org/10.1007/s11042-023-16351-4
5. Deepa BG, Senthil S (2021) Predicting invasive ductal carcinoma tissues in whole slide images of breast Cancer by using convolutional neural network model and multiple classifiers. Multimed Tools Appl 81:8575–8596. https://doi.org/10.1007/s11042-022-12114-9
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