Deep Learning Approaches for Retinal Image Classification: A Comparative Study of GoogLeNet and ResNet Architectures

Yıl 2024, Cilt: 8 Sayı: 2, 123 - 128, 30.12.2024

Kerem Gencer , Gülcan Gencer , İnayet Hakkı Cizmeci

https://doi.org/10.47897/bilmes.1523768

Öz

This study evaluates the performance of four deep learning models, namely GoogLeNet (InceptionV3), ResNet-18, ResNet-50, and ResNet-101, in classifying Optical Coherence Tomography (OCT) images. Images were pre-processed by resizing them to 224x224 pixels and normalizing the pixel values. The models were fine-tuned using pre-trained weights from ImageNet dataset and trained for 10 iterations using categorical_crossentropy loss function and Adam optimizer. Performance metrics such as accuracy, precision, recall, specificity, and F1 score were calculated for each model. The results show that ResNet-101 outperforms other models with 96.69% accuracy, 96.85% sensitivity, and 98.90% specificity. ResNet-50 also showed high performance, while ResNet-18 showed the lowest performance with 33.99% accuracy. GoogLeNet achieved moderate results with 72.21% accuracy. ROC curves and confusion matrices are used to visualize the classification performance. ResNet-101 and ResNet-50 show superior performance in all classes, while ResNet-18 and GoogLeNet have higher misclassification rates. This study highlights the importance of model depth and residual connections in improving the classification performance of OCT images. The findings show that deeper models such as ResNet-50 and ResNet-101 are more effective in capturing complex features, leading to better classification accuracy.

Anahtar Kelimeler

OCT, deep learning, GoogLeNet, ResNet-18, ResNet-50, ResNet-101, retinal disease classification

Kaynakça

• [1] D. S. Kermany et al., "Identifying medical diagnoses and treatable diseases by image-based deep learning," cell, vol. 172, no. 5, pp. 1122-1131. e9, 2018.
• [2] T. S. Apon, M. M. Hasan, A. Islam, and M. G. R. Alam, "Demystifying deep learning models for retinal OCT disease classification using explainable AI," in 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), 2021: IEEE, pp. 1-6.
• [3] A. Khan, K. Pin, A. Aziz, J. W. Han, and Y. Nam, "Optical coherence tomography image classification using hybrid deep learning and ant colony optimization," Sensors, vol. 23, no. 15, p. 6706, 2023.
• [4] F. Li et al., "Deep learning-based automated detection of retinal diseases using optical coherence tomography images," Biomedical optics express, vol. 10, no. 12, pp. 6204-6226, 2019.
• [5] X. Liu et al., "A deep learning based pipeline for optical coherence tomography angiography," Journal of Biophotonics, vol. 12, no. 10, p. e201900008, 2019.
• [6] J. Yoon et al., "Optical coherence tomography-based deep-learning model for detecting central serous chorioretinopathy," Scientific reports, vol. 10, no. 1, p. 18852, 2020.
• [7] G. Litjens et al., "A survey on deep learning in medical image analysis," Medical image analysis, vol. 42, pp. 60-88, 2017.
• [8] S. K. Zhou et al., "A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises," Proceedings of the IEEE, vol. 109, no. 5, pp. 820-838, 2021.
• [9] G. E. Hinton and R. R. Salakhutdinov, "Reducing the dimensionality of data with neural networks," science, vol. 313, no. 5786, pp. 504-507, 2006.
• [10] K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770-778.
• [11] K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014.
• [12] C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi, "Inception-v4, inception-resnet and the impact of residual connections on learning," in Proceedings of the AAAI conference on artificial intelligence, 2017, vol. 31, no. 1.
• [13] J. De Fauw et al., "Clinically applicable deep learning for diagnosis and referral in retinal disease," Nature medicine, vol. 24, no. 9, pp. 1342-1350, 2018.
• [14] V. Gulshan et al., "Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs," jama, vol. 316, no. 22, pp. 2402-2410, 2016.
• [15] C. Mohanty, S. Mahapatra, B. Acharya, F. Kokkoras, V. C. Gerogiannis, I. Karamitsos, and A. Kanavos, "Using deep learning architectures for detection and classification of diabetic retinopathy," Sensors, vol. 23, no. 12, p. 5726, 2023.
• [16] R. Chavan and D. Pete, "Automatic multi-disease classification on retinal images using multilevel glowworm swarm convolutional neural network," Journal of Engineering and Applied Sciences, vol. 71, no. 26, 2024. https://doi.org/10.1186/s44147-023-00335-0
• [17] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, "Rethinking the inception architecture for computer vision," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 2818-2826.
• [18] G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, "Densely connected convolutional networks," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 4700-4708.
• [19] N. Rajagopalan, V. Narasimhan, S. K. Vinjimoor, and J. J. Aiyer, "Deep CNN framework for retinal disease diagnosis using optical coherence tomography images," Journal of Ambient Intelligence and Humanized Computing, vol. 12, pp. 7569–7580, 2021.