1. Anwar, S.M., Majid, M., Qayyum, A., Awais, M., Alnowami, M., Khan, M.K.: Medical image analysis using convolutional neural networks: a review. J. Med. Syst.42(11), 1–13 (Nov. 2018)

   Google Scholar 

2. Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: a deep convolutional encoder-decoder architecture for image segmentation. CoRR, abs/1511.00561 (2015)

   Google Scholar 

3. Bottou, L.: Large-scale machine learning with stochastic gradient descent. In: Lechevallier, Y., Saporta, G. (eds.) Proceedings of COMPSTAT’2010, pp. 177–186. Heidelberg, Physica-Verlag HD (2010)

   Google Scholar 

4. Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Semantic image segmentation with deep convolutional nets and fully connected crfs. In: 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015)

   Google Scholar 

5. Chen, X., Girshick, R.B., He, K., Dollár, P.: Tensormask: a foundation for dense object segmentation. CoRR, abs/1903.12174 (2019)

   Google Scholar 

6. Everingham, M., Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. Int. J. Comput. Vision88(2), 303–338 (2010)

   Article  Google Scholar 

7. Fukushima, K.: Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern.36(4), 193–202 (1980)

   Article  Google Scholar 

8. Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)

   Google Scholar 

9. Girshick, R.: Fast r-cnn. In: Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), ICCV ’15, pages 1440–1448, Washington, DC, USA, (2015). IEEE Computer Society

   Google Scholar 

10. Girshick, R.B., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. CoRR, abs/1311.2524 (2013)

    Google Scholar 

11. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016).http://www.deeplearningbook.org

12. He, K., Gkioxari, G. Dollár P., Girshick, R.: Mask r-cnn. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2980–2988 (2017)

    Google Scholar 

13. He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. CoRR, abs/1406.4729 (2014)

    Google Scholar 

14. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)

    Google Scholar 

15. Huang, G., Liu, Z., Weinberger, K.Q.: Densely connected convolutional networks. CoRR, abs/1608.06993 (2016)

    Google Scholar 

16. Hubel, D.H., Wiesel, T.N.: Receptive fields and functional architecture of monkey striate cortex. J. Physiol. (Lond.)195, 215–243 (1968)

    Article  Google Scholar 

17. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. CoRR, abs/1502.03167 (2015)

    Google Scholar 

18. Karpathy, A., Fei-Fei, L.: Deep visual-semantic alignments for generating image descriptions. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)

    Google Scholar 

19. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprintarXiv:1412.6980 (2014)

20. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 25, pp. 1097–1105. Curran Associates, Inc. (2012)

    Google Scholar 

21. Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE86(11), 2278–2324 (1998)

    Article  Google Scholar 

22. LeCun, Y., Cortes, C.: MNIST handwritten digit database (2010)

    Google Scholar 

23. Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. CoRR, abs/1803.01534, (2018)

    Google Scholar 

24. Maggiori, E., Tarabalka, Y., Charpiat, G., Alliez, P.: Convolutional neural networks for large-scale remote-sensing image classification. IEEE Trans. Geosci. Remote. Sens.55(2), 645–657 (2017)

    Article  Google Scholar 

25. Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on International Conference on Machine Learning, ICML’10, pp. 807–814. USA (2010). Omnipress

    Google Scholar 

26. Ng, A.Y.: Feature selection, l1 versus l2 regularization, and rotational invariance. In: Proceedings of the Twenty-first International Conference on Machine Learning, ICML ’04, pages 78–, New York, NY, USA (2004). ACM

    Google Scholar 

27. Noh, H., Hong, S., Han, B.: Learning deconvolution network for semantic segmentation. CoRR, abs/1505.04366 (2015)

    Google Scholar 

28. Pinheiro, P.H.O., Collobert, R., Dollór, P.: Learning to segment object candidates. CoRR, abs/1506.06204 (2015)

    Google Scholar 

29. Pinheiro, P.H.O., Lin, T., Collobert, R., Dollór, P.: Learning to refine object segments. CoRR, abs/1603.08695 (2016)

    Google Scholar 

30. Rasti, P., Uiboupin, T., Escalera, S., Anbarjafari, G.: Convolutional neural network super resolution for face recognition in surveillance monitoring, vol. 9756, pp. 175–184 (2016)

    Google Scholar 

31. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788 (2016)

    Google Scholar 

32. Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: towards real-time object detection with region proposal networks. In: Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 28, pp. 91–99. Curran Associates, Inc. (2015)

    Google Scholar 

33. Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention (MICCAI), volume 9351 of LNCS, pp. 234–241. Springer, 2015. Available onarXiv:1505.04597 [cs.CV]

34. Ruder, S.: An overview of gradient descent optimization algorithms. CoRR, abs/1609.04747 (2016)

    Google Scholar 

35. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. In: Rumelhart, D.E., Mcclelland, J.L. (eds.) Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations, pp. 318–362. MIT Press, Cambridge, MA (1986)

    Google Scholar 

36. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. (IJCV)115(3), 211–252 (2015)

    Article MathSciNet  Google Scholar 

37. Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell.39(4), 640–651 (2017)

    Article  Google Scholar 

38. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR, abs/1409.1556 (2014)

    Google Scholar 

39. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res.15(1), 1929–1958 (2014)

    MathSciNet MATH  Google Scholar 

40. Sufian, A., Ghosh, A., Naskar, A., Sultana, F.: Bdnet: bengali handwritten numeral digit recognition based on densely connected convolutional neural networks. CoRR, abs/1906.03786 (2019)

    Google Scholar 

41. Sultana, F., Sufian, A., Dutta, P.: Advancements in image classification using convolutional neural network. In: 2018 Fourth International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), pp. 122–129 (2018)

    Google Scholar 

42. Sultana, F., Sufian, A., Dutta, P.: A review of object detection models based on convolutional neural network. CoRR, abs/1905.01614 (2019)

    Google Scholar 

43. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)

    Google Scholar 

44. Wang, T., Wu, D.J., Coates, A., Ng, A.Y.: End-to-end text recognition with convolutional neural networks. In: Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), pp. 3304–3308 (2012)

    Google Scholar 

45. Zaitoun, N.M., Aqel, M.J.: Survey on image segmentation techniques. Procedia Comput. Sci.65, 797- 806 (2015). International Conference on Communications, management, and Information technology (ICCMIT’2015)

    Google Scholar 

46. Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) Computer Vision - ECCV 2014. pp, pp. 818–833. Springer International Publishing, Cham (2014)

    Google Scholar 

47. Zhang, R., Isola, P., Efros, A.A.: Colorful image colorization. CoRR, abs/1603.08511 (2016)

    Google Scholar 

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