Diabetic Retinopathy Detection Using Quantum Transfer Learning
- URL: http://arxiv.org/abs/2405.01734v1
- Date: Thu, 2 May 2024 21:09:39 GMT
- Title: Diabetic Retinopathy Detection Using Quantum Transfer Learning
- Authors: Ankush Jain, Rinav Gupta, Jai Singhal,
- Abstract summary: Diabetic Retinopathy (DR), a prevalent complication in diabetes patients, can lead to vision impairment due to lesions formed on the retina.
We propose a hybrid quantum transfer learning technique to detect DR.
Our model has shown remarkable results, achieving an accuracy of 97% for ResNet-18.
- Score: 2.724141845301679
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Diabetic Retinopathy (DR), a prevalent complication in diabetes patients, can lead to vision impairment due to lesions formed on the retina. Detecting DR at an advanced stage often results in irreversible blindness. The traditional process of diagnosing DR through retina fundus images by ophthalmologists is not only time-intensive but also expensive. While classical transfer learning models have been widely adopted for computer-aided detection of DR, their high maintenance costs can hinder their detection efficiency. In contrast, Quantum Transfer Learning offers a more effective solution to this challenge. This approach is notably advantageous because it operates on heuristic principles, making it highly optimized for the task. Our proposed methodology leverages this hybrid quantum transfer learning technique to detect DR. To construct our model, we utilize the APTOS 2019 Blindness Detection dataset, available on Kaggle. We employ the ResNet-18, ResNet34, ResNet50, ResNet101, ResNet152 and Inception V3, pre-trained classical neural networks, for the initial feature extraction. For the classification stage, we use a Variational Quantum Classifier. Our hybrid quantum model has shown remarkable results, achieving an accuracy of 97% for ResNet-18. This demonstrates that quantum computing, when integrated with quantum machine learning, can perform tasks with a level of power and efficiency unattainable by classical computers alone. By harnessing these advanced technologies, we can significantly improve the detection and diagnosis of Diabetic Retinopathy, potentially saving many from the risk of blindness. Keywords: Diabetic Retinopathy, Quantum Transfer Learning, Deep Learning
Related papers
- Towards Transfer Learning for Large-Scale Image Classification Using
Annealing-based Quantum Boltzmann Machines [7.106829260811707]
We present an approach to employ Quantum Annealing (QA) in image classification.
We propose using annealing-based Quantum Boltzmann Machines as part of a hybrid quantum-classical pipeline.
We find that our approach consistently outperforms its classical baseline in terms of test accuracy and AUC-ROC-Score.
arXiv Detail & Related papers (2023-11-27T16:07:49Z) - Bridging Classical and Quantum Machine Learning: Knowledge Transfer From
Classical to Quantum Neural Networks Using Knowledge Distillation [0.0]
This paper introduces a new method to transfer knowledge from classical to quantum neural networks using knowledge distillation.
We adapt classical convolutional neural network (CNN) architectures like LeNet and AlexNet to serve as teacher networks.
Quantum models achieve an average accuracy improvement of 0.80% on the MNIST dataset and 5.40% on the more complex Fashion MNIST dataset.
arXiv Detail & Related papers (2023-11-23T05:06:43Z) - ShadowNet for Data-Centric Quantum System Learning [188.683909185536]
We propose a data-centric learning paradigm combining the strength of neural-network protocols and classical shadows.
Capitalizing on the generalization power of neural networks, this paradigm can be trained offline and excel at predicting previously unseen systems.
We present the instantiation of our paradigm in quantum state tomography and direct fidelity estimation tasks and conduct numerical analysis up to 60 qubits.
arXiv Detail & Related papers (2023-08-22T09:11:53Z) - Improving Classification of Retinal Fundus Image Using Flow Dynamics
Optimized Deep Learning Methods [0.0]
Diabetic Retinopathy (DR) refers to a barrier that takes place in diabetes mellitus damaging the blood vessel network present in the retina.
It can take some time to perform a DR diagnosis using color fundus pictures because experienced clinicians are required to identify the tumors in the imagery used to identify the illness.
arXiv Detail & Related papers (2023-04-29T16:11:34Z) - Globally Optimal Training of Neural Networks with Threshold Activation
Functions [63.03759813952481]
We study weight decay regularized training problems of deep neural networks with threshold activations.
We derive a simplified convex optimization formulation when the dataset can be shattered at a certain layer of the network.
arXiv Detail & Related papers (2023-03-06T18:59:13Z) - Problem-Dependent Power of Quantum Neural Networks on Multi-Class
Classification [83.20479832949069]
Quantum neural networks (QNNs) have become an important tool for understanding the physical world, but their advantages and limitations are not fully understood.
Here we investigate the problem-dependent power of QCs on multi-class classification tasks.
Our work sheds light on the problem-dependent power of QNNs and offers a practical tool for evaluating their potential merit.
arXiv Detail & Related papers (2022-12-29T10:46:40Z) - Blindness (Diabetic Retinopathy) Severity Scale Detection [0.0]
Diabetic retinopathy (DR) is a severe complication of diabetes that can cause permanent blindness.
Timely diagnosis and treatment of DR are critical to avoid total loss of vision.
We propose a novel deep learning based method for automatic screening of retinal fundus images.
arXiv Detail & Related papers (2021-10-04T11:31:15Z) - A quantum algorithm for training wide and deep classical neural networks [72.2614468437919]
We show that conditions amenable to classical trainability via gradient descent coincide with those necessary for efficiently solving quantum linear systems.
We numerically demonstrate that the MNIST image dataset satisfies such conditions.
We provide empirical evidence for $O(log n)$ training of a convolutional neural network with pooling.
arXiv Detail & Related papers (2021-07-19T23:41:03Z) - An Interpretable Multiple-Instance Approach for the Detection of
referable Diabetic Retinopathy from Fundus Images [72.94446225783697]
We propose a machine learning system for the detection of referable Diabetic Retinopathy in fundus images.
By extracting local information from image patches and combining it efficiently through an attention mechanism, our system is able to achieve high classification accuracy.
We evaluate our approach on publicly available retinal image datasets, in which it exhibits near state-of-the-art performance.
arXiv Detail & Related papers (2021-03-02T13:14:15Z) - Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis
and Uncertainty Quantification [0.0]
Diabetic Retinopathy (DR) is one of the microvascular complications of Diabetes Mellitus, which remains one of the leading causes of blindness worldwide.
Computational models based on Conal Neural Networks represent the state of the art for the automatic detection of DR using eye fundus images.
In this paper, a hybrid Deep Learning-Gaussian process method for DR diagnosis and uncertainty quantification is presented.
arXiv Detail & Related papers (2020-07-29T04:10:42Z) - Towards Efficient Processing and Learning with Spikes: New Approaches
for Multi-Spike Learning [59.249322621035056]
We propose two new multi-spike learning rules which demonstrate better performance over other baselines on various tasks.
In the feature detection task, we re-examine the ability of unsupervised STDP with its limitations being presented.
Our proposed learning rules can reliably solve the task over a wide range of conditions without specific constraints being applied.
arXiv Detail & Related papers (2020-05-02T06:41:20Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.