A Robust Framework Combining Image Processing and Deep Learning Hybrid Model to Classify Cardiovascular Diseases Using a Limited Number of Paper-Based Complex ECG Images
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Kaniz Fatema1, Sidratul Montaha1, Md Awlad Hossen Rony1
1Health Informatics Research Lab, Department of Computer Science and Engineering, Daffodil International University, Dhaka 1207, Bangladesh.
Insights
This study introduces a deep learning model for early heart disease detection using electrocardiogram (ECG) images. The hybrid InRes-106 model achieved 98.34% accuracy, outperforming individual models for accurate cardiac diagnosis.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Imaging
Background:
- Early detection of heart disease is crucial for effective treatment and improved patient outcomes.
- Electrocardiogram (ECG) images are vital tools for diagnosing cardiovascular conditions.
- Automated systems can enhance the accuracy and efficiency of cardiac disease classification.
Purpose of the Study:
- To develop a deep learning system for classifying cardiac diseases from paper-based ECG images.
- To achieve high accuracy and low time complexity in automated heart disease prediction.
- To compare the performance of established deep learning models with a novel integrated model.
Main Methods:
- Utilized five deep learning models: InceptionV3, ResNet50, MobileNetV2, VGG19, and DenseNet201.
- Introduced a hybrid InRes-106 model by integrating InceptionV3 and ResNet50 as a deep convolutional neural network.
- Applied image pre-processing techniques, ablation studies, and k-fold cross-validation for model optimization and evaluation.
Main Results:
- The proposed InRes-106 model achieved a testing accuracy of 98.34%.
- Individual models showed varying accuracies: InceptionV3 (90.56%), ResNet50 (89.63%), DenseNet201 (88.94%), VGG19 (87.87%), and MobileNetV2 (80.56%).
- The hybrid model demonstrated superior performance despite a limited dataset size.
Conclusions:
- The integrated InRes-106 deep learning model effectively diagnoses cardiac diseases from ECG images with high accuracy.
- The study highlights the potential of advanced deep learning techniques in improving early cardiac disease detection.
- Image pre-processing and model fine-tuning are essential for robust performance in automated ECG analysis.
Abstract:
Heart disease can be life-threatening if not detected and treated at an early stage. The electrocardiogram (ECG) plays a vital role in classifying cardiovascular diseases, and often physicians and medical researchers examine paper-based ECG images for cardiac diagnosis. An automated heart disease prediction system might help to classify heart diseases accurately at an early stage. This study aims to classify cardiac diseases into five classes with paper-based ECG images using a deep learning approach with the highest possible accuracy and the lowest possible time complexity. This research consists of two approaches. In the first approach, five deep learning models, InceptionV3, ResNet50, MobileNetV2, VGG19, and DenseNet201, are employed. In the second approach, an integrated deep learning model (InRes-106) is introduced, combining InceptionV3 and ResNet50. This model is developed as a deep convolutional neural network capable of extracting hidden and high-level features from images. An ablation study is conducted on the proposed model altering several components and hyperparameters, improving the performance even further. Before training the model, several image pre-processing techniques are employed to remove artifacts and enhance the image quality. Our proposed hybrid InRes-106 model performed best with a testing accuracy of 98.34%. The InceptionV3 model acquired a testing accuracy of 90.56%, the ResNet50 89.63%, the DenseNet201 88.94%, the VGG19 87.87%, and the MobileNetV2 achieved 80.56% testing accuracy. The model is trained with a k-fold cross-validation technique with different k values to evaluate the robustness further. Although the dataset contains a limited number of complex ECG images, our proposed approach, based on various image pre-processing techniques, model fine-tuning, and ablation studies, can effectively diagnose cardiac diseases.
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