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

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.

A Computer-Aided Diagnostic System to Identify Diabetic Retinopathy, Utilizing a Modified Compact Convolutional Transformer and Low-Resolution Images to Reduce Computation Time

“…A false positive ( FP ) is an outcome where the model incorrectly predicts the positive class, while a false negative ( FN ) is an outcome where the model incorrectly predicts the negative class. For this research, we calculated the accuracy ( ACC ), precision, recall, specificity, F1-score, false positive rate ( FPR ), false negative rate ( FNR ), false discovery rate ( FDR ), negative predicted value ( NPV ), and the Matthew correlation coefficient ( MCC ) to evaluate the model’s performance [ 27 , 34 ]. …”

“…The MobileNetV2 model consists of 53 layers and has 3.5 million trainable parameters [ 34 ]. It is made up of two types of blocks, each with three layers.…”

“…They were designed for sequence modeling and transduction activities. The success of the transformer model in NLP has inspired researchers to extend it to computer vision, where it has shown promising results in image classification [ 34 ]. Several transformer models are also utilized in this study.…”

A Lightweight Robust Deep Learning Model Gained High Accuracy in Classifying a Wide Range of Diabetic Retinopathy Images

“…The convolutional layers consume a large proportion of the computational, whereas the time pooling and fully connected layers only consume 5 to 10 percent of the computational time [73], [74]. We therefore focus on the time complexity of the convolutional layers; see Table 5 and 6 [74], [75], [76]. We compute the theoretical time complexity, which is defined in [73] as follows:…”

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