Abstract
The most common disease that is found among people across the world is Diabetes and it is predicted to increase more in the upcoming years by The World Health Organization (WHO). People who are diabetic for a longer period are more likely to have Diabetic Retinopathy (DR), an eye disease which can lead to blindness and this cannot be reversed. One of the severe stage problems of DR is Retinal Neovascularization (RN), i.e., outburst of retinal blood vessels. Residual Network (ResNet) has an effective technique called Skip or Residual Connections which solves the problem of vanishing gradient during backpropagation. ResNet50 has 50 layers which is a deep network that omits signal representations and learns from residual representations leading to predict RN with 88.97% accuracy.
References
Kwasigroch, Arkadiusz, Bartlomiej Jarzembinski, and Michal Grochowski. “Deep CNN Based Decision Support System for Detection and Assessing the Stage of Diabetic Retinopathy.” 2018 International Interdisciplinary PhD Workshop (IIPhDW), 2018. https://doi.org/10.1109/iiphdw.2018.8388337.
Omar, Z. A., M. Hanafi, S. Mashohor, N. F. Mahfudz, and M. Muna'im. “Automatic Diabetic Retinopathy Detection and Classification System.” 2017 7th IEEE International Conference on System Engineering and Technology (ICSET), 2017. https://doi.org/10.1109/icsengt.2017.8123439.
Zhou, Zhengyang. “Attention Based Stack ResNet for Citywide Traffic Accident Prediction.” 2019 20th IEEE International Conference on Mobile Data Management (MDM), 2019. https://doi.org/10.1109/mdm.2019.00-27.
Tian, Xiao, and Chao Chen. “Modulation Pattern Recognition Based on Resnet50 Neural Network.” 2019 IEEE 2nd International Conference on Information Communication and Signal Processing (ICICSP), 2019. https://doi.org/10.1109/icicsp48821.2019.8958555.
Jena, Manaswini, Smita Prava Mishra, and Debahuti Mishra. “Detection of Diabetic Retinopathy Images Using a Fully Convolutional Neural Network.” 2018 2nd International Conference on Data Science and Business Analytics (ICDSBA), 2018. https://doi.org/10.1109/icdsba.2018.00103.
K, Harihanth, and Karthikeyan B. “Diabetic Retinopathy Detection Using Ensemble Deep Learning and Individual Channel Training.” 2020 3rd International Conference on Intelligent Sustainable Systems (ICISS), 2020. https://doi.org/10.1109/iciss49785.2020.9315991.
Akter, Morium, Mohammad Shorif Uddin, and Mahmudul Hasan Khan. “Morphology-Based Exudates Detection from Color Fundus Images in Diabetic Retinopathy.” 2014 International Conference on Electrical Engineering and Information & Communication Technology, 2014. https://doi.org/10.1109/iceeict.2014.6919124.
Pujitha, Appan K., Gamalapati S. Jahnavi, and Jayanthi Sivaswamy. “Detection of Neovascularization in Retinal Images Using Semi-Supervised Learning.” 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), 2017. https://doi.org/10.1109/isbi.2017.7950613.
S, Valarmathi, and Vijayabhanu R. “A Survey on Diabetic Retinopathy Disease Detection and Classification Using Deep Learning Techniques.” 2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII), 2021. https://doi.org/10.1109/icbsii51839.2021.9445163.
Zahisham, Zharfan, Chin Poo Lee, and Kian Ming Lim. “Food Recognition with Resnet-50.” 2020 IEEE 2nd International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), 2020. https://doi.org/10.1109/iicaiet49801.2020.9257825.
Roy, Projapoti, Md. Main Oddin Chisty, and H.M. Abdul Fattah. “Alzheimer’s Disease Diagnosis from MRI Images Using ResNet-152 Neural Network Architecture.” 2021 5th International Conference on Electrical Information and Communication Technology (EICT), 2021. https://doi.org/10.1109/eict54103.2021.9733507.
Li, Xin, and Laxmisha Rai. “Apple Leaf Disease Identification and Classification Using Resnet Models.” 2020 IEEE 3rd International Conference on Electronic Information and Communication Technology (ICEICT), 2020. https://doi.org/10.1109/iceict51264.2020.9334214.
Sridhar, Sashank, Rahul Seetharaman, and Sowmya Sanagavarapu. “Intelligent Vision-Based Malware Classification Using Quantised ResNets.” 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), 2021. https://doi.org/10.1109/iemcon53756.2021.9623219.
Ibrahim, Younis, Haibin Wang, Man Bai, Zhi Liu, Jianan Wang, Zhiming Yang, and Zhengming Chen. “Soft Error Resilience of Deep Residual Networks for Object Recognition.” IEEE Access 8 (2020): 19490–503. https://doi.org/10.1109/access.2020.2968129.
Das, Arijit, and Srinibas Rana. “Exploring Residual Networks for Breast Cancer Detection from Ultrasound Images.” 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), 2021. https://doi.org/10.1109/icccnt51525.2021.9580160.