Acknowledgement
This research was funded by the Deanship of Scientific Research (DSR) at King Ab-dulaziz University, Jeddah, Saudi Arabia. The authors, therefore, gratefully acknowledge the DSR for their technical and financial support.
References
- Zhuang, F., Z. Qi, K. Duan, D. Xi, Y. Zhu, H. Zhu, H. Xiong and Q. He., A comprehensive survey on transfer learning. Proceedings of the IEEE, 2020. 109(1): p. 43-76.
- Kandel, I. and M. Castelli, Transfer learning with convolutional neural networks for diabetic retinopathy image classification. A review. Applied Sciences, 2020. 10(6): p. 2021. https://doi.org/10.3390/app10062021
- Gopalakrishnan, K., S. K. Khaitan, A. Choudhary and A. Agrawal., Deep convolutional neural networks with transfer learning for computer vision-based data-driven pavement distress detection. Construction and Building Materials, 2017. 157: p. 322-330. https://doi.org/10.1016/j.conbuildmat.2017.09.110
- Pham, T.D., Classification of COVID-19 chest X-rays with deep learning: new models or fine tuning? Health Information Science and Systems, 2021. 9(1): p. 1-11.
- Islam, M. A., M. N. R. Shuvo, M. Shamsojjaman, S. Hasan, M. S. Hossain and T. Khatun., An Automated Convolutional Neural Network Based Approach for Paddy Leaf Disease Detection. International Journal of Advanced Computer Science and Applications,, 2021. 12(1): p. 280-288.
- Cuan, K., T. Zhang, J. Huang, C. Fang and Y. Guan., Detection of avian influenza-infected chickens based on a chicken sound convolutional neural network. Computers and Electronics in Agriculture, 2020. 178: p. 105688. https://doi.org/10.1016/j.compag.2020.105688
- Ayyachamy, S., V. Alex, M. Khened and G. Krishnamurthi. Medical image retrieval using Resnet-18. in Medical Imaging 2019: Imaging Informatics for Healthcare, Research, and Applications. 2019. International Society for Optics and Photonics.
- Ebrahimi, A., S. Luo, and R. Chiong. Introducing Transfer Leaming to 3D ResNet-18 for Alzheimer's Disease Detection on MRI Images. in 2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ). 2020. IEEE.
- Pham, T.D., A comprehensive study on classification of COVID-19 on computed tomography with pretrained convolutional neural networks. Scientific Reports, 2020. 10(1): p. 1-8. https://doi.org/10.1038/s41598-019-56847-4
- Ou, X., P. Yan, Y. Zhang, B. Tu, G. Zhang, J. Wu and W. Li., Moving object detection method via ResNet-18 with encoder-decoder structure in complex scenes. IEEE Access, 2019. 7: p. 108152-108160. https://doi.org/10.1109/ACCESS.2019.2931922
- Zhou, Y., F. Ren, S. Nishide and X. Kang. Facial Sentiment Classification Based on Resnet-18 Model. in 2019 International Conference on Electronic Engineering and Informatics (EEI). 2019. IEEE.
- Novitasari, D. C. R., R. Hendradi, R. E. Caraka, Y. Rachmawati, N. Z. Fanani, A. Syarifudin, T. Toharudin and R. C. Chen., Detection of covid-19 chest x-ray using support vector machine and convolutional neural network. Commun. Math. Biol. Neurosci., 2020. 2020: p. Article ID 42.
- Sharifrazi, D., R. Alizadehsani, M. Roshanzamir, J. H. Joloudari, A. Shoeibi, M. Jafari, S. Hussain, Z. A. Sani, F. Hasanzadeh and F. Khozeimeh., Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images. Biomedical Signal Processing and Control, 2021: p. 102622.
- Win, K. Y., N. Maneerat, K. Hamamoto and S. Sreng., Hybrid Learning of Hand-Crafted and Deep-Activated Features Using Particle Swarm Optimization and Optimized Support Vector Machine for Tuberculosis Screening. Applied Sciences, 2020. 10(17): p. 5749. https://doi.org/10.3390/app10175749
- Talo, M., O. Yildirim, U. B. Baloglu, G. Aydin and U. R. Acharya., Convolutional neural networks for multi-class brain disease detection using MRI images. Computerized Medical Imaging and Graphics, 2019. 78: p. 101673. https://doi.org/10.1016/j.compmedimag.2019.101673
- Mahbod, A., G. Schaefer, C. Wang, R. Ecker and I. Ellinge. Skin lesion classification using hybrid deep neural networks. in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2019. IEEE.
- Kang, C., Y. Huo, L. Xin, B. Tian and B. Yu., Feature selection and tumor classification for microarray data using relaxed Lasso and generalized multi-class support vector machine. Journal of theoretical biology, 2019. 463: p. 77-91. https://doi.org/10.1016/j.jtbi.2018.12.010
- Mohammed, A., A. Ahmed, W. Mohammed, G. Viju and M. Taha, Mammogram Images Classification using Linear Discriminant Analysis. 2020. International Research Journal of Engineering and Technology (IRJET), 2020. 7(6):p.6656- 6662 .
- Ahmed, A. and S. Malebary, Feature selection and the fusion-based method for enhancing the classification accuracy of SVM for breast cancer detection. Int. J. Comput. Sci. Netw. Secur., 2019. 19(11): p. 55. https://doi.org/10.22937/IJCSNS.2019.19.11.8
- Ahmed, A. and O.B. El Sadig, Heterogeneous multiclassifier method based on weighted voting for Breast Cancer Detection. Int. J. Adv. Sci. Eng. Technol., 2019. 7(4): p. 36-41.
- Ibrahim, A. O., A. Ahmed, A. Abdu, R. Abd-alaziz, M. A. Alobeed, A. Y. Saleh and A. Elsafi, Classification of Mammogram Images Using Radial Basis Function Neural Network. in International Conference of Reliable Information and Communication Technology. 2019. Springer. : . p.311-320
- Cervantes, J., F. Garcia-Lamont, L. Rodriguez-Mazahua and A. Lopez., A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing, 2020. 408: p. 189-215. https://doi.org/10.1016/j.neucom.2019.10.118
- He, K., X. Zhang, S. Ren and J. Sun. Deep residual learning for image recognition. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
- ImageNet. http://www.image-net.org
- Dang-Nguyen, M. Lux and P. T. Schmidt. Kvasir: A multiclass image dataset for computer aided gastrointestinal disease detection. in Proceedings of the 8th ACM on Multimedia Systems Conference. 2017.
- Mendoncya, T., P. Ferreira, J. Marques, A. Marcyal and J. Rozeira., A dermoscopic image database for research and benchmarking. Presentation in proceedings of PH2 IEEE EMBC, 2013.
- Jha, D., P. H. Smedsrud, M. A. Riegler, P. Halvorsen, T. de Lange, D. Johansen and H. D. Johansen. Kvasir-seg: A segmented polyp dataset. in International Conference on Multimedia Modeling. 2020. Springer.
- Ozturk, S. and U. Ozkaya, Residual LSTM layered CNN for classification of gastrointestinal tract diseases. Journal of Biomedical Informatics, 2021. 113: p. 103638. https://doi.org/10.1016/j.jbi.2020.103638
- Ahmed, A. and S.J. Malebary, Query Expansion Based on Top-Ranked Images for Content-Based Medical Image Retrieval. IEEE Access, 2020. 8: p. 194541-194550. https://doi.org/10.1109/ACCESS.2020.3033504
- Ahmed, A., Implementing Relevance Feedback for ContentBased Medical Image Retrieval. IEEE Access, 2020. 8: p. 79969-79976. https://doi.org/10.1109/ACCESS.2020.2990557
- Zhong, J., W. Wang, H. Wu, Z. Wen and J. Qin. PolypSeg: An Efficient Context-Aware Network for Polyp Segmentation from Colonoscopy Videos. in International Conference on Medical Image Computing and Computer-Assisted Intervention. 2020. Springer.
- Ma, Y., X. Chen, and B. Sun. Polyp detection in colonoscopy videos by bootstrapping via temporal consistency. in 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). 2020. IEEE.
- Bernal, J., N. Tajkbaksh, F. J. Sanchez, B. J. Matuszewski, H. Chen, L. Yu, Q. Angermann, O. Romain, B. Rustad and I. Balasingham., Comparative validation of polyp detection methods in video colonoscopy: results from the MICCAI 2015 endoscopic vision challenge. IEEE transactions on medical imaging, 2017. 36(6): p. 1231-1249. https://doi.org/10.1109/TMI.2017.2664042
- Yu, L., H. Chen, Q. Dou, J. Qin and P. A. Heng., Integrating online and offline three-dimensional deep learning for automated polyp detection in colonoscopy videos. IEEE journal of biomedical and health informatics, 2016. 21(1): p. 65-75. https://doi.org/10.1109/JBHI.2016.2637004
- Codella, N. C., D. Gutman, M. E. Celebi, B. Helba, M. A. Marchetti, S. W. Dusza, A. Kalloo, K. Liopyris, N. Mishra and H. Kittler. Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic). in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). 2018. IEEE.
- Jianu, S.R.S., L. Ichim, and D. Popescu. Automatic diagnosis of skin cancer using neural networks. in 2019 11th International Symposium on Advanced Topics in Electrical Engineering (ATEE). 2019. IEEE.
- Menegola, A., J. Tavares, M. Fornaciali, L. T. Li, S. Avila and E. Valle., RECOD titans at ISIC challenge 2017. arXiv preprint arXiv:1703.04819, 2017.