Segmentation and Classification of Glaucoma Using U-Net with Deep Learning Model

Journal of Healthcare Engineering - Tập 2022 - Trang 1-10 - 2022
M.B. Sudhan1, M. Sinthuja2, S. Pravinth Raja3, J. Amutharaj4, G. Charlyn Pushpa Latha5, S. Sheeba Rachel6, T. Anitha5, T. Rajendran7, Yosef Asrat Waji8
1Department of Artificial Intelligence and Machine Learning, MVJ College of Engineering, Bangalore, Karnataka, India
2Department of Information Science and Engineering, M. S. Ramaiah Institute of Technology, Bangalore, Karnataka, India
3Department of Computer Science & Engineering, Presidency University, Bangalore, Karnataka, India
4Department of Information Science and Engineering, RajaRajeswari College of Engineering, Mysore Road, Bangalore, Karnataka, India
5Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (Deemed to be University), Chennai, Tamilnadu, India
6Department of Information Technology, Sri Sairam Engineering College (Autonomous), Chennai, Tamilnadu, India
7Makeit Technologies, Coimbatore, Tamilnadu, India
8Department of Chemical Engineering, College of Biological and Chemical Engineering, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia

Tóm tắt

Glaucoma is the second most common cause for blindness around the world and the third most common in Europe and the USA. Around 78 million people are presently living with glaucoma (2020). It is expected that 111.8 million people will have glaucoma by the year 2040. 90% of glaucoma is undetected in developing nations. It is essential to develop a glaucoma detection system for early diagnosis. In this research, early prediction of glaucoma using deep learning technique is proposed. In this proposed deep learning model, the ORIGA dataset is used for the evaluation of glaucoma images. The U-Net architecture based on deep learning algorithm is implemented for optic cup segmentation and a pretrained transfer learning model; DenseNet-201 is used for feature extraction along with deep convolution neural network (DCNN). The DCNN approach is used for the classification, where the final results will be representing whether the glaucoma infected or not. The primary objective of this research is to detect the glaucoma using the retinal fundus images, which can be useful to determine if the patient was affected by glaucoma or not. The result of this model can be positive or negative based on the outcome detected as infected by glaucoma or not. The model is evaluated using parameters such as accuracy, precision, recall, specificity, and F-measure. Also, a comparative analysis is conducted for the validation of the model proposed. The output is compared to other current deep learning models used for CNN classification, such as VGG-19, Inception ResNet, ResNet 152v2, and DenseNet-169. The proposed model achieved 98.82% accuracy in training and 96.90% in testing. Overall, the performance of the proposed model is better in all the analysis.

Từ khóa


Tài liệu tham khảo

10.1109/access.2016.2596761

S. Anindita, 2015, Automatic glaucoma detection based on the type of features used: a review, Journal of Theoretical and Applied Information Technology, 72, 366

10.1016/j.compmedimag.2019.101657

10.1080/08820538.2016.1229801

10.1186/s12938-020-00767-2

10.25077/jitce.4.02.105-109.2020

10.1109/TMI.2019.2927226

10.1371/journal.pone.0209409

10.1007/s11042-019-7460-4

10.1186/s12938-019-0649-y

10.3390/app10144916

M. Mateen, 2019, Fundus image classification using VGG-19 architecture with PCA and SVD, Symmetry, 11, 1, 10.3390/sym11010001

10.1007/978-3-030-13969-8_6

10.3390/app10113777

R. Mahum, 2022, A novel hybrid approach based on deep CNN to detect glaucoma using fundus imaging, Electronics, 11, 10.3390/electronics11010026

S. Gheisari, 2021, A combined convolutional and recurrent neural network for enhanced glaucoma detection, Scientific Reports, 11, 1

Z. Zhang, 2010, ORIGA(-light): an online retinal fundus image database for glaucoma analysis and research, Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2010, 3065

E. A. M. Yasir, 2020, Classification of diabetic retinopathy using stacked autoencoder-based deep neural network, Journal of Computational Science and Intelligent Technologies, 1, 9, 10.53409/mnaa.jcsit1102

C. Narmatha, 2021, An analysis of deep learning techniques in neuroimaging, Journal of Computational Science and Intelligent Technologies, 2, 7, 10.53409/mnaa/jcsit/2102

P. Manimegalai, 2021, A study on diabetic retinopathy detection using image processing, Journal of Computational Science and Intelligent Technologies, 2, 21, 10.53409/mnaa/jcsit/2104

10.1080/07391102.2020.1788642

Thông tin
Thông tin xuất bản

Journal of Healthcare Engineering

Tập 2022

1-10

Thông tin tác giả