Detection of pulmonary pathologies using convolutional neural networks, Data Augmentation, ResNet50 and Vision Transformers
This addresses the need for accurate and fast diagnostic tools for pulmonary diseases like cancer and pneumonia, but it appears incremental as it combines existing techniques on a new dataset.
The paper tackled the problem of detecting lung pathologies from medical images using a hybrid method combining CNNs, Data Augmentation, ResNet50, and Vision Transformers, achieving an accuracy of 98% and an AUC of 99%, outperforming existing methods.
Pulmonary diseases are a public health problem that requires accurate and fast diagnostic techniques. In this paper, a method based on convolutional neural networks (CNN), Data Augmentation, ResNet50 and Vision Transformers (ViT) is proposed to detect lung pathologies from medical images. A dataset of X-ray images and CT scans of patients with different lung diseases, such as cancer, pneumonia, tuberculosis and fibrosis, is used. The results obtained by the proposed method are compared with those of other existing methods, using performance metrics such as accuracy, sensitivity, specificity and area under the ROC curve. The results show that the proposed method outperforms the other methods in all metrics, achieving an accuracy of 98% and an area under the ROC curve of 99%. It is concluded that the proposed method is an effective and promising tool for the diagnosis of pulmonary pathologies by medical imaging.