Аңдатпа
In a recent day, we could witness an explosive growth of artificial intelligence and deep learning in medical applications. With the increased availability of medical images, deep learning tools can provide a necessary diagnostic utility. However, current DNN models have shown high variations in their performance to each medical image datasets. In this study, we proposed ensemble learning to achieve synergistic improvements in model accuracy and thereby provide highly stabilized performance on diverse medical datasets. We first investigated the model performance of the latest deep learning architectures, e.g., Inception, VGGNet, MobileNet, Xception, ResNet50, and selected 7 state-of-the-art models to the diverse open CT datasets (SARS-COV-2 CT-Scan, USCD CT, and COVID-X dataset). The model parameters were transferred from the other domain and fine-tuned based on medical image sets. The last convolutional layers were stacked and a fully-connected neural network is employed to find generalized feature space. The peak accuracy of the fine-tuned single CNN models were InceptionV3 - 0.96, VGG16 - 0.94, VGG19 - 0.94, MobileNetV2 - 0.98, Xception - 0.9, ResNet - 0.96, DenseNet201 - 0.97. The proposed ensemble model achieves the peak accuracy of 0.99%, outperforming each individual model and achieving the highest performance in all three open CT datasets. Experimental results demonstrated that the proposed ensemble model is able to represent the hierarchical features and thereby it improves the stability and reproducibility of the classifier models.