Comparison between Deep Learning and Conventional Machine Learning in Classifying Iliofemoral Deep Venous Thrombosis upon CT Venography.
Jung Han HwangJae Won SeoJeong Ho KimSuyoung ParkYoung Jae KimYeoun Jae KimPublished in: Diagnostics (Basel, Switzerland) (2022)
In this study, we aimed to investigate quantitative differences in performance in terms of comparing the automated classification of deep vein thrombosis (DVT) using two categories of artificial intelligence algorithms: deep learning based on convolutional neural networks (CNNs) and conventional machine learning. We retrospectively enrolled 659 participants (DVT patients, 282; normal controls, 377) who were evaluated using contrast-enhanced lower extremity computed tomography (CT) venography. Conventional machine learning consists of logistic regression (LR), support vector machines (SVM), random forests (RF), and extreme gradient boosts (XGB). Deep learning based on CNN included the VGG16, VGG19, Resnet50, and Resnet152 models. According to the mean generated AUC values, we found that the CNN-based VGG16 model showed a 0.007 higher performance (0.982 ± 0.014) as compared with the XGB model (0.975 ± 0.010), which showed the highest performance among the conventional machine learning models. In the conventional machine learning-based classifications, we found that the radiomic features presenting a statistically significant effect were median values and skewness. We found that the VGG16 model within the deep learning algorithm distinguished deep vein thrombosis on CT images most accurately, with slightly higher AUC values as compared with the other AI algorithms used in this study. Our results guide research directions and medical practice.
Keyphrases
- deep learning
- machine learning
- artificial intelligence
- convolutional neural network
- contrast enhanced
- computed tomography
- dual energy
- big data
- diffusion weighted
- magnetic resonance imaging
- image quality
- positron emission tomography
- magnetic resonance
- healthcare
- diffusion weighted imaging
- end stage renal disease
- newly diagnosed
- primary care
- prognostic factors
- climate change
- single cell
- high throughput
- optical coherence tomography
- mass spectrometry