PMC

Schematic illustration of the pre-processing of electrocardiography and application of deep learning. ECG electrocardiography, VT ventilatory threshold, DLT deep learning threshold, DL deep learning, VO₂ oxygen uptake, CPX cardiopulmonary exercise testing.

Fitted normal curves on accuracy distributions of the four networks. Normal curves are fitted on the accuracy distributions of the clinical deep learning network (blue), the structural connectivity deep learning network (orange), morphology deep learning network (purple) and the clinical-MRI combined deep learning network (gray), based on 16 randomly selected subjects (repeated 10,000 times) from the first evaluation set (n = 32). The mean accuracies (dashed lines) of these distributions were 68.7%, 62.5%, 62.4% and 84.4% for the clinical deep learning network, structural connectivity deep learning network, morphology deep learning network and clinical-MRI combined deep learning network, respectively. A paired t-test showed significant differences between accuracies of each pair of networks (all p < 0.001).

Fitted normal curves on accuracy distributions of seven networks. Normal curves are fitted on the accuracy distributions of the clinical deep learning network (orange), the structural connectivity deep learning network (blue), morphology deep learning network (purple), clinical-structural connectivity deep learning network (pink), clinical- morphology deep learning network (green), structural connectivity- morphology deep learning network (yellow) and the clinical-MRI combined deep learning network (gray), based on 16 randomly selected subjects (repeated 10,000 times) from the first evaluation set (n = 32). The mean accuracies (dashed lines) of these distributions were 68.7%, 62.5%, 62.4%, 78,0%, 81.2%, 78,1%, and 84.4% for the deep learning networks mentioned, respectively. A paired t-test showed significant differences between accuracies of each pair of networks (all p < 0.001).

In this research work we have compared the Deep Hybrid Learning with both XGBoost and Random Forest variant, with a conventional Deep Neural Network (without transfer learning and having the same 21 layered CNN as DHL), DenseNet201 with transfer learning, InceptionNet v3 with transfer learning, ResNet50 with transfer learning and VGG16 with transfer learning. ‘Normal’ and ‘Cancer’ has been denoted as 0 and 1 respectively in the confusion matrices. 1. Deep Hybrid Learning with Random Forest 2. Deep Hybrid Learning with XGBoost 3. Conventional Deep Neural Network Model 4. DenseNet201 with transfer learning 5. ResNet50 with transfer learning 6. InceptionNetv3 with transfer learning 7. VGG16 with transfer learning.