International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 11 | Nov 2024
p-ISSN: 2395-0072
www.irjet.net
Transfer Learning model with Ensemble Learning to detect Diabetic Retinopathy from retinal images, enhancing early diagnosis: A Survey Meenal Katole1, Prof. Pramila M Chawan2 1MTech student, Dept of Computer Engineering and IT, VJTI college Mumbai, Maharashtra, India
2Associate Professor, Dept of Computer Engineering and IT, VJTI college Mumbai, Maharashtra, India
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Abstract - Diabetic retinopathy (DR) is one of the primary
this process can be demanding, take a significant amount of time, and be prone to mistakes , especially in large-scale screenings. To address these challenges, automated diagnostic systems based on deep learning have gained significant attention in past years. Deep learning, machine learning subset, has the ability to analyze large datasets of retinal images, making it highly suitable for detecting subtle signs of DR that may be overlooked by the human eye. These systems can not only accelerate the diagnostic process but also improve accuracy and consistency in identifying various stages of DR. Among the key advancements in this field are transfer learning and ensemble learning techniques. Transfer learning allows the use of pre-trained models are initially trained on extensive datasets for various tasks adapts them for DR detection. This approach significantly reduces the amount of training data and computational resources needed, while still achieving high accuracy. Pre-trained models can be fine-tuned to identify specific retinal features associated with DR.
causes of blindness among adults globally, making early diagnosis essential for preventing vision impairment. Recent progress in machine learning, especially in the areas of trans fer learning and ensemble learning, presents valuable opportunities for automating the detection of DR through retinal imaging. This paper reviews current research and methodologies that utilize these techniques to enhance the precision and reliability of DR diagnosis, emphasizing the importance of improving early detection and treatment outcomes. Additionally, the paper explores data augmentation techniques that address the challenge of small and imbalanced datasets, a common issue in medical imaging. The dataset size can be artificially increased by applying transformations like image rotation and flipping, and scaling, the models can generalize better and improve their detection capabilities. With the ongoing advancements in deep learning, these hybrid approaches have the potential to make automated DR detection a standard tool in clinical settings, improving the speed and accuracy of diagnoses and ultimately reducing the global bur den of diabetic-related blindness. Transfer learning leverages pre-trained models, enabling faster and more accurate detection even with limited datasets, while ensemble learning combines multiple models to increase diagnostic robustness and reduce error rates. This paper surveys recent research on the application of these advanced techniques in DR detection, emphasizing their potential to improve early diagnosis and patient outcomes. By integrating the strengths of both transfer and ensemble learning, more robust and scalable models can be developed, paving the way for efficient, automated DR screening systems.
1. INTRODUCTION
Ensemble learning, on the other hand, focuses on combining the strengths of many models to develop a more robust and reliable system. By considering predictions from various ensemble methods can address the weaknesses or biases of individual models, resulting in more accurate and well-rounded diagnoses. Techniques such as bagging, boosting, and stacking are commonly used in ensemble learning to refine DR detection systems. This survey delves into the integration of trans fer learning and ensemble learning for building more powerful and reliable DR detection models. By leveraging the advantages of both techniques, researchers aim to develop systems that are not only more accurate but also scalable for widespread clinical use. The use of such hybrid models has the potential to revolutionize early DR diagnosis, enabling earlier intervention and improving treatment outcomes for patients at risk of vision loss.
1.1 Diabetic Retinopathy
1.2 Challenges in Diabetic Retinopathy Detection
Diabetic retinopathy (DR) is a severe diabetes complication that harms the blood vessels in the retina, potentially causing significant vision impairment and even blindness if left untreated. As the prevalence of diabetes rises globally, the need for effective and timely diagnosis of DR becomes increasingly critical. Traditionally, ophthalmologists manually examine retinal images to diagnose DR. However,
Several challenges arise in the automatic detection of DR: Data Scarcity: Medical image datasets are often small, limiting the working of deep learning models.
Key Words: Diabetic Retinopathy, Transfer Learning, Ensemble Learning, Deep Learning, Retinal Images, Medical Imaging.
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Impact Factor value: 8.315
Class Imbalance: Diabetic retinopathy is less frequent in early stages, leading to imbalanced datasets.
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