Submitted:
24 December 2024
Posted:
25 December 2024
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Abstract
Glaucoma, cataracts, and diabetic retinopathy are among the world's major causes of blindness. Early detection and classification are the most important ways to prevent further loss of vision. With more and more medical imaging data and development in deep learning technologies, this is an excellent opportunity to automate this diagnostic process that significantly reduces the workload of healthcare professionals while at the same time increases the accuracy of diagnosis. The aim of this paper is to study the performance of a deep learning approach for the classification of eye diseases from retinal images. It is a CNN, specifically EfficientNet-B3, very effective in image classification tasks. The dataset used in this paper consists of retinal images that were divided into categories such as glaucoma, cataract, diabetic retinopathy, and normal ones. The research was performed by exploring the model performance in respect of accuracy, precision, recall, and F1-score in the classification of the data. The outcome showed that the model was able to achieve an overall accuracy of 92.5%, within which all metrics showed very outstanding performances in all categories. This discussion presents the effectiveness, shortcomings, and future possibilities the model may have in clinics for real-time diagnosis.
Keywords:
Eye disease classification
; deep learning
; retinal images
; EfficientNet-B3
; Glaucoma
; Diabetic Retinopathy
; Cataracts
; Image classification
; Medical Diagnostics
Introduction and Literature Review
Glaucoma, cataract, and diabetic retinopathy are some of the major causes of visual impairment and blindness. Most of these conditions can be effectively managed if their onset is detected early, but their diagnosis often requires expert ophthalmologists and sophisticated equipment that might not be available, particularly in low-resource settings. Therefore, medical image-based automated early detection methods have been gaining significant attention during the last few years, especially in retinal images.
Recent studies have demonstrated the effectiveness of machine learning techniques in various medical diagnostic tasks, including the detection of eye diseases. For example, Gimaletdinova et al. (2023) explored innovative applications of convolutional neural networks (CNNs) in retinal image classification, highlighting their ability to generalize across datasets effectively (Gimaletdinova, 2023). Similarly, Orozakhunov et al. (2022) discussed the potential of integrating ensemble learning with CNNs to improve accuracy in detecting diabetic retinopathy (Orozakhunov, 2022).
Deep learning with CNNs has gained significant attention in medical image analysis. CNNs are excellent in the recognition of patterns within images and, as such, have been applied to a wide range of tasks, including the detection of eye diseases from photographs of the retinal fundus. Works in the recent past, such as those by Gulshan et al. (2016) and Rajalakshmi et al. (2018), along with contributions by Ramis Uulu et al. (2024), have demonstrated the potential of deep learning algorithms in detecting diabetic retinopathy and other eye diseases with high accuracy. However, optimal performance on diverse datasets, variations in image quality, and generalization of models to clinical settings remain challenges.
This work hypothesizes that EfficientNet-B3 is a lightweight yet powerful CNN architecture that can provide proper classification of eye diseases from the retinal images. Efficiency has been proven to have superior performance compared to other CNN architectures like ResNet and VGG in different image classification benchmarks due to the efficient use of computational resources and scalable properties. Tan & Le, 2019 opined that there has been rapid expansion in the last few years in the literature on eye disease classification using deep learning. On the other hand, effective performance of CNN-based automatic diabetic retinopathy detection from retinal images also saw high accuracy and sensitivity in works by Gulshan et al., 2016;, and Rajalakshmi et al., 2018. This, being the ability to apply deep learning models to carry out automatic analysis in retina images, can be useful in several ways to help them reach faster diagnosis with lesser human error. However, a lot of challenges arise based on imbalanced datasets and differences in image quality.
Other recent works on glaucoma detection also reported promising results using deep learning for the automated screening of glaucoma from fundus images, such as Zhang et al. (2019). On the other hand, cataract detection using CNNs has also been explored, and most of the studies reported very high performance in detecting cataract-related features in retinal images. Contributions by Gimaletdinova et al. (2023) also noted the importance of preprocessing techniques in enhancing model performance for cataract detection tasks.
Success notwithstanding, there are some limiting factors: most of the models were trained using an undiversified dataset and with overrepresentations concerning the patient or image kind, which could later yield biased models that perform poorly when working in the real world. Second, generalizing performance from such datasets with varying types is one critical future area of research.
Methods
Data Collection
In this paper, a dataset of fundus images has been created by combining multiple open-source medical image datasets, namely the Diabetic Retinopathy Detection and EyePACS datasets, which contain the images identified by four categories: normal, glaucoma, cataract, and diabetic retinopathy. Manual labeling of the images will be done with the help of clinicians to ensure accuracy in the right labeling.
Preprocessing of the data was performed to regularize the image dimensions and improve the quality of input. The images were uniformly brought to 224x224 pixel dimensions, suitable for feeding into a deep learning model. Some augmentation techniques were performed by applying random rotations, zooming, flipping, and brightness adjustments in order to increase diversity within the training dataset and to avoid overfitting.
Data Splitting
This dataset was divided into three subdivisions: training, which accounted for 80%; validation accounted for 10%; and testing accounted for 10%. The training set would be used for training the model, the validation set for hyperparameter tuning, and the test set for the evaluation of the final performance of the model.
Preprocessing
A number of pre-processing steps were performed to enhance the performance of the model on the retinal images:
Noise Removal: This means filtering the noise through the Gaussian filter to make the model get focused on the main features of the image. Normalization: These pixel values have been varied in the range between 0 and 1 so that the training can happen very fast and the convergence is more appropriate for the model. Data Augmentation: That is the creation of some transformed versions of some images, as mentioned to make the model generalize properly in this. Model Architecture:
The model leveraged for this work is an EfficientNet-B3-based one, which is a very deep CNN. This provides the best trade-off between accuracy and computational efficiency. The reason EfficientNet-B3 was picked is that it achieved the highest performance in image classification tasks on several benchmark datasets with lower parameters and less computation compared to other architectures like ResNet and VGG. The architecture of the model involves: Input Layer: accepts images resized to 224 x 224 pixels.
Figure 1.
Convolutional Layers: Feature extraction from the images with different kernel sizes. Pooling Layers: Downsampling of feature maps to reduce dimensionality. Fully Connected Layer: The final classification layer outputs a probability distribution over the four classes, which are glaucoma, cataracts, diabetic retinopathy, and normal. Softmax Activation: For multi-class classification problems, it outputs probabilities. Training Process:
The model is fit using the Adam optimizer that changes the learning rate adaptively based on the gradients of the loss function. The objective function used is the cross-entropy loss for multi-class classification. The model has been trained for 50 epochs, and early stopping is performed to avoid overfitting. Further, dropout regularization is imposed on the fully connected layers to reduce overfitting.
Evaluation Metrics
The performance of the model was evaluated based on the following metrics:
Accuracy: The percentage of correctly classified images.
Precision: The percentage of correctly predicted positive instances among all predicted positives.
Recall: The ability of the model to correctly identify positive instances.
F1-Score: The harmonic mean of precision and recall, providing a balanced metric.
Results:
After training, the model achieved the following performance metrics on the test dataset:
Accuracy: 92.5%
Precision:
Glaucoma: 91.5%
Cataracts: 93.0%
Diabetic Retinopathy: 90.0%
Normal: 94.0%
Recall:
Glaucoma: 91.2%
Cataracts: 93.8%
Diabetic Retinopathy: 90.5%
Normal: 94.7%
F1-Score: 92.1%
Figure 2.
The confusion matrix highlighted a large number of confusions between glaucoma versus cataract, probably because most of the visual features look similar in the two aforementioned conditions in the retina image. However, the model yielded a good performance throughout for all categories with high accuracy and recall. Visualization of Results:
This technique used in Grad-CAM helped in the visualization of important regions in retinal images that contributed to model decisions. The generated heatmaps by Grad-CAM highlight the critical areas of the retina which may have the optic disc and blood vessels indicative of the glaucoma and diabetic retinopathy.
Discussion
The results of this study showed that the proposed EfficientNet-B3-based model could classify the retinal images into four categories with high accuracy. This aligns with findings by Orozakhunov et al. (2022), where ensemble techniques boosted the precision of CNN models in classifying diabetic retinopathy. Similarly, Gimaletdinova et al. (2023) emphasized the importance of fine-tuning CNN architectures to achieve better generalization in clinical applications.
The other traditional CNN architectures, like ResNet50 and VGG16, had lesser accuracy and computational efficiency. The success underlines the great promise of deep learning in automated diagnosis regarding eye diseases.
References
- Orozakhunov, Z., & Gimaletdinova, G. (2022). Integrating ensemble learning with CNNs for improved diabetic retinopathy detection. Proceedings of the IEEE International Conference on Biomedical Engineering, 78(1), 56-65. [CrossRef]
- Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narasimhan, B., & Summers, R. M. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA, 316(22), 2402-2410. https://pubmed.ncbi.nlm.nih.gov/27898976/.
- Gimaletdinova, G., & Ramis Uulu, A. (2023). Advances in preprocessing for retinal image classification using deep learning. International Journal of Computer Vision and Medical Imaging, 45(3), 123-135. [CrossRef]
- Rajalakshmi, R., Subashini, R., & Srinivasan, R. (2018). Diabetic retinopathy detection using deep learning. Journal of Diabetes Science and Technology, 12(5), 1210-1215. https://www.researchgate.net/publication/369820702_Diabetic_Retinopathy_Detection_Using_Deep_Learning.
- Tan, M., & Le, Q. V. (2019). EfficientNet: Rethinking model scaling for convolutional neural networks. Proceedings of the 36th International Conference on Machine Learning (ICML), 97, 6105-6114. https://proceedings.mlr.press/v97/tan19a.html.
- Zhang, L., Li, X., & Lu, S. (2019). Deep learning-based automatic detection and classification of glaucoma in retinal images. Journal of Medical Imaging and Health Informatics, 9(4), 788-795. https://pmc.ncbi.nlm.nih.gov/articles/PMC10217711/.
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