Open Access
ITM Web Conf.
Volume 46, 2022
International Conference on Engineering and Applied Sciences (ICEAS’22)
Article Number 05001
Number of page(s) 6
Section Data Analysis and Image Processing
Published online 06 June 2022
  1. A. Chater et A. Lasfar, « Comparison of robust methods for extracting descriptors and facial matching », in 2019 International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS), Fez, Morocco, avr. 2019, p. 1-4. doi: 10.1109/WITS.2019.8723858. [Google Scholar]
  2. A. Chater et A. Lasfar, « New approach to the identification of the easy expression recognition system by robust techniques (SIFT, PCA-SIFT, ASIFT and SURF) », TELKOMNIKA Telecommun. Comput. Electron. Control, vol. 18, no 2, p. 695, avr. 2020, doi: 10.12928/telkomnika.v18i2.13726. [Google Scholar]
  3. A. Chater, A. Lasfar, et A. Et-Tahir, « Face Recognition Using Feature Extraction and Similarity Measures », vol. 62, no 03, p. 10, 2020. [Google Scholar]
  4. M. O. Khairandish, M. Sharma, V. Jain, J. M. Chatterjee, et N. Z. Jhanjhi, « A Hybrid CNN-SVM Threshold Segmentation Approach for Tumor Detection and Classification of MRI Brain Images », IRBM, p. S1959031821000713, juin 2021, doi: 10.1016/j.irbm.2021.06.003. [Google Scholar]
  5. A. Saha, M. Hosseinzadeh, et H. Huisman, « End- to-end prostate cancer detection in bpMRI via 3D CNNs: Effects of attention mechanisms, clinical priori and decoupled false positive reduction », Med. Image Anal., vol. 73, p. 102155, oct. 2021, doi: 10.1016/ [CrossRef] [Google Scholar]
  6. L. Duran-Lopez et al., « Wide & Deep neural network model for patch aggregation in CNN-based prostate cancer detection systems », Comput. Biol. Med., vol. 136, p. 104743, sept. 2021, doi: 10.1016/j.compbiomed.2021.104743. [CrossRef] [Google Scholar]
  7. Y. Su, D. Li, et X. Chen, « Lung Nodule Detection based on Faster R-CNN Framework », Comput. Methods Programs Biomed., vol. 200, p. 105866, mars 2021, doi: 10.1016/j.cmpb.2020.105866. [CrossRef] [Google Scholar]
  8. C. B. Gonçalves, J. R. Souza, et H. Fernandes, « CNN architecture optimization using bio-inspired algorithms for breast cancer detection in infrared images », Comput. Biol. Med., vol. 142, p. 105205, mars 2022, doi: 10.1016/j.compbiomed.2021.105205. [CrossRef] [Google Scholar]
  9. M. Desai et M. Shah, « An anatomization on breast cancer detection and diagnosis employing multi- layer perceptron neural network (MLP) and Convolutional neural network (CNN) », Clin. EHealth, vol. 4, p. 1-11, 2021, doi: 10.1016/j.ceh.2020.11.002. [Google Scholar]
  10. G. Jia, H.-K. Lam, et Y. Xu, « Classification of COVID-19 chest X-Ray and CT images using a type of dynamic CNN modification method », Comput. Biol. Med., vol. 134, p. 104425, juill. 2021, doi: 10.1016/j.compbiomed.2021.104425. [Google Scholar]
  11. S. Thakur et A. Kumar, « X-ray and CT-scan-based automated detection and classification of covid-19 using convolutional neural networks (CNN) », Biomed. Signal Process. Control, vol. 69, p. 102920, août 2021, doi: 10.1016/j.bspc.2021.102920. [Google Scholar]
  12. Md. Z. Islam, Md. M. Islam, et A. Asraf, « A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images », Inform. Med. Unlocked, vol. 20, p. 100412, 2020, doi: 10.1016/j.imu.2020.100412. [Google Scholar]
  13. K. H. Shibly, S. K. Dey, M. T.-U. Islam, et M. M. Rahman, « COVID faster R–CNN: A novel framework to Diagnose Novel Coronavirus Disease (COVID-19) in X-Ray images », Inform. Med. Unlocked, vol. 20, p. 100405, 2020, doi: 10.1016/j.imu.2020.100405. [Google Scholar]
  14. M. E. H. Chowdhury et al., « Can AI Help in Screening Viral and COVID-19 Pneumonia? », IEEE Access, vol. 8, p. 132665-132676, 2020, doi: 10.1109/ACCESS.2020.3010287. [CrossRef] [Google Scholar]
  15. T. Rahman et al., « Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images », Comput. Biol. Med., vol. 132, p. 104319, mai 2021, doi: 10.1016/j.compbiomed.2021.104319. [CrossRef] [Google Scholar]
  16. Gonzalez, Woods (2008), Intensity Transformations and Spatial Filtering p. 127 [Google Scholar]

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