COMPARISON OF PROPOSED DCNN MODEL WITH STANDARD CNN ARCHITECTURES FOR RETINAL DISEASES CLASSIFICATION CNN for Retinal Disease Classification
Main Article Content
Keywords
Retinal image classification, convolutional neural network, deep learning, choroidal neovascularization, drusen, diabetic macular edema, Novel Medical Image Analysis and Detection network (MIDNet 18).
Abstract
Deep learning in medical image analysis has indicated increasing interest in the classification of signs of abnormalities. In this study, a new CNN architecture (MIDNet18) Medical Image Detection Network was proposed for the classification of retinal diseases using OCT images. The model consists of fourteen convolutional layers, Seven Max Pooling layers, four dense layers, and one classification layer. A multi-class classification layer in the MIDNet18 is used to classify the OCT images into either normal or any of the four abnormal types: Choroidal Neovascularization (CNV), Drusen, and Diabetic Macular Edema (DME). The dataset consists of 83484 training images, 41741 validation images, and 968 test images. According to the experimental results, MIDNet18 obtains an accuracy of 98.86%, and their performances are compared with other standard CNN models; ResNet-50 (83.26%), MobileNet (93.29%) and DenseNet (92.5%). Also, MIDNet18 has been Proved to be statistically significant with as p-value < 0.001 than other standard CNN architectures in classifying retinal diseases using OCT images.
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Bahrami, Abass, Alireza Karimian, Emad Fatemizadeh, Hossein Arabi, and Habib Zaidi. 2020. “A New Deep Convolutional Neural Network Design with Efficient Learning Capability: Application to CT Image Synthesis from MRI.” Medical Physics 47 (10): 5158–71.
Bhatt, Dulari, Chirag Patel, Hardik Talsania, Jigar Patel, Rasmika Vaghela, Sharnil Pandya, Kirit Modi, and Hemant Ghayvat. 2021. “CNN Variants for Computer Vision: History, Architecture, Application, Challenges and Future Scope.” Electronics. https://doi.org/10.3390/electronics10202470.
Brownlee, Jason. 2019. Deep Learning for Computer Vision: Image Classification, Object Detection, and Face Recognition in Python. Machine Learning Mastery.
Fang, Leyuan, Chong Wang, Shutao Li, Hossein Rabbani, Xiangdong Chen, and Zhimin Liu. 2019. “Attention to Lesion: Lesion-Aware Convolutional Neural Network for Retinal Optical Coherence Tomography Image Classification.” IEEE Transactions on Medical Imaging 38 (8): 1959–70.
Hang, Siang Thye, and Masaki Aono. 2017. “Bi-Linearly Weighted Fractional Max Pooling.” Multimedia Tools and Applications. https://doi.org/10.1007/s11042-017-4840-5.
Ikuno, Yasushi, Yukari Jo, Toshimitsu Hamasaki, and Yasuo Tano. 2010. “Ocular Risk Factors for Choroidal Neovascularization in Pathologic Myopia.” Investigative Ophthalmology & Visual Science 51 (7): 3721–25.
Kepp, Timo, Jan Ehrhardt, Mattias P. Heinrich, Gereon Huttmann, and Heinz Handels. 2019. “Topology-Preserving Shape-Based Regression Of Retinal Layers In Oct Image Data Using Convolutional Neural Networks.” 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). https://doi.org/10.1109/isbi.2019.8759261.
Koziarski, Michal. 2021. “Two-Stage Resampling for Convolutional Neural Network Training in the Imbalanced Colorectal Cancer Image Classification.” 2021 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/ijcnn52387.2021.9533998.
Kremelberg, David. 2010. Practical Statistics: A Quick and Easy Guide to IBM® SPSS® Statistics, STATA, and Other Statistical Software. SAGE Publications, Incorporated.
Mateen, Muhammad, Junhao Wen, Nasrullah, Sun Song, and Zhouping Huang. 2018. “Fundus Image Classification Using VGG-19 Architecture with PCA and SVD.” Symmetry. https://doi.org/10.3390/sym11010001.
Miller, David G., and Lawrence J. Singerman. 2006. “Vision Loss in Younger Patients: A Review of Choroidal Neovascularization.” Optometry and Vision Science: Official Publication of the American Academy of Optometry 83 (5): 316–25.
Mittal, Praveen. 2020. “Automatic Classification of Retinal Pathology in Optical Coherence Tomography Scan Images Using Convolutional Neural Network.” Journal of Advanced Research in Dynamical and Control Systems. https://doi.org/10.5373/jardcs/v12sp3/20201337.
Mobeen-ur-Rehman, Mobeen-ur-Rehman, Sharzil Haris Khan, Zeeshan Abbas, and S. M. Danish Rizvi. 2019. “Classification of Diabetic Retinopathy Images Based on Customised CNN Architecture.” 2019 Amity International Conference on Artificial Intelligence (AICAI). https://doi.org/10.1109/aicai.2019.8701231.
Modi, Shraddha, Rajib Guhathakurta, Sheeba Praveen, Sachin Tyagi, and Saket Narendra Bansod. 2021. “Detail-Oriented Capsule Network for Classification of CT Scan Images Performing the Detection of COVID-19.” Materials Today. Proceedings, July. https://doi.org/10.1016/j.matpr.2021.07.367.
Mohan, Ramya, Kirupa Ganapathy, and Rama A. 2022. “Brain Tumour Classification of Magnetic Resonance Images Using a Novel CNN-Based Medical Image Analysis and Detection Network in Comparison to VGG16.” Journal of Population Therapeutics and Clinical Pharmacology = Journal de La Therapeutique Des Populations et de La Pharamcologie Clinique 28 (2): e113–25.
Mooney, Paul. n.d. “Retinal OCT Images (optical Coherence Tomography).” Accessed March 17, 2022. https://www.kaggle.com/paultimothymooney/kermany2018.
Ranjbarzadeh, Ramin, Abbas Bagherian Kasgari, Saeid Jafarzadeh Ghoushchi, Shokofeh Anari, Maryam Naseri, and Malika Bendechache. 2021. “Brain Tumor Segmentation Based on Deep Learning and an Attention Mechanism Using MRI Multi-Modalities Brain Images.” Scientific Reports 11 (1): 10930.
Rong, Yibiao, Dehui Xiang, Weifang Zhu, Kai Yu, Fei Shi, Zhun Fan, and Xinjian Chen. 2019. “Surrogate-Assisted Retinal OCT Image Classification Based on Convolutional Neural Networks.” IEEE Journal of Biomedical and Health Informatics 23 (1): 253–63.
Shafiq, Shakeel, and Tayyaba Azim. 2021. “Introspective Analysis of Convolutional Neural Networks for Improving Discrimination Performance and Feature Visualisation.” PeerJ. Computer Science 7 (May): e497.
Shanmugamani, Rajalingappaa. 2018. Deep Learning for Computer Vision: Expert Techniques to Train Advanced Neural Networks Using TensorFlow and Keras. Packt Publishing Ltd.
Sun, Jing, Cheng Wan, Jun Cheng, Fengli Yu, and Jiang Liu. 2017. “Retinal Image Quality Classification Using Fine-Tuned CNN.” Fetal, Infant and Ophthalmic Medical Image Analysis. https://doi.org/10.1007/978-3-319-67561-9_14.
Trucco, Emanuele, Tom MacGillivray, and Yanwu Xu. 2019. Computational Retinal Image Analysis: Tools, Applications and Perspectives. Academic Press.
Tsuji, Takumasa, Yuta Hirose, Kohei Fujimori, Takuya Hirose, Asuka Oyama, Yusuke Saikawa, Tatsuya Mimura, et al. 2020. “Classification of Optical Coherence Tomography Images Using a Capsule Network.” BMC Ophthalmology 20 (1): 114.
Valizadeh, Amin, Saeid Jafarzadeh Ghoushchi, Ramin Ranjbarzadeh, and Yaghoub Pourasad. 2021. “Presentation of a Segmentation Method for a Diabetic Retinopathy Patient’s Fundus Region Detection Using a Convolutional Neural Network.” Computational Intelligence and Neuroscience 2021 (July): 7714351.
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Published
Aug 31, 2022
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Mohan, R., Ganapathy, K., & Arunmozhi, R. (2022). COMPARISON OF PROPOSED DCNN MODEL WITH STANDARD CNN ARCHITECTURES FOR RETINAL DISEASES CLASSIFICATION : CNN for Retinal Disease Classification. Journal of Population Therapeutics and Clinical Pharmacology, 29(03). https://doi.org/10.47750/jptcp.2022.945
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