Multi-Scale Feature Pyramid for Detection of Red Lesions in Fundus Images

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Goutam Kumar Ghorai
Swagata Kundu
Gautam Sarkar
Ashis Kumar Dhara

Abstract

Diabetic retinopathy (DR) is increasing rapidly around the world, but there is a shortage of experienced ophthalmologists. Therefore, computer-based diagnosis of the fundus images is essential to screening of referable DR. Automated detection of red lesions is very important for screening of DR. This paper deals with a novel method for automatic detection of red lesion. The main contribution is developing a deep learning based detection framework to handle severe class imbalance and imbalance in sizes of red lesions. The multi-scale features are extracted using the feature pyramid network. A pyramid of features is generated with strong semantics. The proposed network is end-to-end trainable in image level with several scales and works for a wide range of red lesions with acceptable performance. Sensitivity of the proposed method is 0.76 with six false-positive per image on test set of publicly available DIARECTDB1 database and outperforms state-of-the-art approaches. A potential benefit with deep learning based detection framework could be used in screening programs of referable DR. 

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[1]
Goutam Kumar Ghorai, Swagata Kundu, Gautam Sarkar, and Ashis Kumar Dhara , Trans., “Multi-Scale Feature Pyramid for Detection of Red Lesions in Fundus Images”, IJRTE, vol. 12, no. 4, pp. 14–19, Dec. 2023, doi: 10.35940/.
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How to Cite

[1]
Goutam Kumar Ghorai, Swagata Kundu, Gautam Sarkar, and Ashis Kumar Dhara , Trans., “Multi-Scale Feature Pyramid for Detection of Red Lesions in Fundus Images”, IJRTE, vol. 12, no. 4, pp. 14–19, Dec. 2023, doi: 10.35940/.
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References

Ting, D. S. W., Pasquale, L. R., Peng, L., Campbell, J. P., Lee, A. Y., Raman, R., Tan, G.S.W., Schmetterer, L., Keane, P.A. and Wong, T.Y. & Wong, T. Y. (2019). Artificial intelligence and deep learning in ophthalmology. British Journal of Ophthalmology, 103 (2), 167-175. https://doi.org/10.1136/bjophthalmol-2018-313173

Raman, R., Srinivasan, S., Virmani, S., Sivaprasad, S., Rao, C., & Rajalakshmi, R. (2019). Fundus photograph-based deep learning algorithms in detecting diabetic retinopathy. Eye, 33(1), 97-109. https://doi.org/10.1038/s41433-018-0269-y

Fleming, A. D., Philip, S., Goatman, K. A., Olson, J. A., & Sharp, P. F. (2006). Automated microaneurysm detection using local contrast normalization and local vessel detection. IEEE transactions on medical imaging, 25(9), 1223-1232. https://doi.org/10.1109/TMI.2006.879953

Bae, J. P., Kim, K. G., Kang, H. C., Jeong, C. B., Park, K. H., & Hwang, J. M. (2011). A study on hemorrhage detection using hybrid method in fundus images. Journal of digital imaging, 24, 394-404. https://doi.org/10.1007/s10278-010-9274-9

Lazar, I., & Hajdu, A. (2012). Retinal microaneurysm detection through local rotating cross-section profile analysis. IEEE transactions on medical imaging, 32(2), 400-407. https://doi.org/10.1109/TMI.2012.2228665

Wang, S., Tang, H. L., Hu, Y., Sanei, S., Saleh, G. M., & Peto, T. (2016). Localizing microaneurysms in fundus images through singular spectrum analysis. IEEE Transactions on Biomedical Engineering, 64(5), 990-1002. https://doi.org/10.1109/TBME.2016.2585344

Fleming, A. D., Philip, S., Goatman, K. A., Olson, J. A., & Sharp, P. F. (2006). Automated microaneurysm detection using local contrast normalization and local vessel detection. IEEE transactions on medical imaging, 25(9), 1223-1232. https://doi.org/10.1109/TMI.2006.879953

Giancardo, L., Mériaudeau, F., Karnowski, T. P., Tobin, K. W., Li, Y., & Chaum, E. (2010, March). Microaneurysms detection with the radon cliff operator in retinal fundus images. In Medical Imaging 2010: Image Processing (Vol. 7623, pp. 292-299). SPIE. https://doi.org/10.1117/12.844442

Quellec, G., Lamard, M., Josselin, P. M., Cazuguel, G., Cochener, B., & Roux, C. (2008). Optimal wavelet transforms for the detection of microaneurysms in retina photographs. IEEE transactions on medical imaging, 27(9), 1230-1241. https://doi.org/10.1109/TMI.2008.920619

Kar, S. S., & Maity, S. P. (2017). Automatic detection of retinal lesions for screening of diabetic retinopathy. IEEE Transactions on Biomedical Engineering, 65(3), 608-618. https://doi.org/10.1109/TBME.2017.2707578

Girshick, R. (2015). Fast r-cnn. In Proceedings of the IEEE international conference on computer vision (pp. 1440-1448). https://doi.org/10.1109/ICCV.2015.169

He, K., Zhang, X., Ren, S., & Sun, J. (2015). Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE transactions on pattern analysis and machine intelligence, 37(9), 1904-1916. https://doi.org/10.1109/TPAMI.2015.2389824

Girshick, R. (2015). Fast R-CNN. In Proceedings of the IEEE international conference on computer vision (pp. 1440-1448). https://doi.org/10.1109/ICCV.2015.169

Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28.

Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., & Berg, A. C. (2016). SSD: Single shot multibox detector. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14 (pp. 21-37). Springer International Publishing. https://doi.org/10.11648/j.sd.20160404.17

Fang, W., Wang, L., & Ren, P. (2019). Tinier-YOLO: A real-time object detection method for constrained environments. IEEE Access, 8, 1935-1944. https://doi.org/10.1109/ACCESS.2019.2961959

Van Grinsven, M. J., van Ginneken, B., Hoyng, C. B., Theelen, T., & Sánchez, C. I. (2016). Fast convolutional neural network training using selective data sampling: Application to hemorrhage detection in color fundus images. IEEE transactions on medical imaging, 35(5), 1273-1284. https://doi.org/10.1109/TMI.2016.2526689

Lin, T. Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2017). Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision (pp. 2980-2988). https://doi.org/10.1109/ICCV.2017.324

Lin, T. Y., Dollár, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017). Feature pyramid networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2117-2125). https://doi.org/10.1109/CVPR.2017.106

Kauppi, T., Kalesnykiene, V., Kamarainen, J. K., Lensu, L., Sorri, I., Raninen, A., Voutilainen, R., Uusitalo, H., Kälviäinen, H. & Pietilä, J. (2007, September). The diaretdb1 diabetic retinopathy database and evaluation protocol. In BMVC (Vol. 1, No. 1, p. 10). https://doi.org/10.5244/C.21.15

Seoud, L., Hurtut, T., Chelbi, J., Cheriet, F., & Langlois, J. P. (2015). Red lesion detection using dynamic shape features for diabetic retinopathy screening. IEEE transactions on medical imaging, 35(4), 1116-1126. https://doi.org/10.1109/TMI.2015.2509785

P, S. R., Rao, B., Anala, J., & Dangayach, M. (2022). Object Detection using Different Point Feature Techniques: A Comparative Analysis. In International Journal of Innovative Technology and Exploring Engineering (Vol. 11, Issue 12, pp. 1–4). https://doi.org/10.35940/ijitee.l9308.11111222

Koul, S. (2020). Contribution of Artificial Intelligence and Virtual Worlds towards development of Super Intelligent AI Agents. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 5, pp. 800–809). https://doi.org/10.35940/ijeat.e9923.069520

Farooq, M., & Khan, M. H. (2019). Pattern Recognition in Digital Images using Fractals. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 2, pp. 3180–3183). https://doi.org/10.35940/ijeat.b4229.129219

Zainudin*, M. N. S., Kee, Y. J., Idris, M. I., Kamaruddin, M. R., & Ramlee, R. H. (2019). Recognizing the Activity Daily Living (ADL) for Subject Independent. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 3, pp. 5422–5427). https://doi.org/10.35940/ijrte.b2381.098319

G., M., Salomi, M., & Priya, R. L. (2020). Pattern Recognition and Stylometry Analysis of Pathittrupathu in Tamil Literature. In International Journal of Management and Humanities (Vol. 5, Issue 2, pp. 10–15). https://doi.org/10.35940/ijmh.b1143.105220

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