2 天前 - … With the increasing availability of specialized X-ray scanners and the development
of deep learning and image processing techniques, it is now possible to collect a large …

2 天前 - … of machine learning models, specifically a pretrained ResNet-50 model and a general
SqueezeNet model, in diagnosing tuberculosis (TB) using chest X-ray … like deep learning …

2 天前 - … In this section, we evaluate the ability of our framework to prevent catastrophic
forgetting in chest X-ray classification. The proposed framework proved its effectiveness in …

2 天前 - … This study highlights the potential power of deep learning in lung nodule detection
and segmentation. It underscores the importance of standardized data processing, code and …

2 天前 - … Chest X-ray images are known to be extremely helpful in the investigation of a
numerous … Optimal hyperparameter selection of deep learning models for covid-19 chest x-ray …

3 天前 - … as crucial for facilitating robust deep learning processes. The NIH Clinic’s Chest
X-ray dataset boasts an extensive collection of over 112,120 chest X-ray images derived from …

3 天前 - … of lung lesions. We present our deep learning-based solution aimed at improving
lung … about the benefits but also about the limitations of machine learning and AI in medicine. …

3 天前 - … A chest X-ray (CXR) is considered the gold standard in … obtained using a portable
X-ray machine. However, Torsy et al… Few studies have employed deep learning to localize an …

4 天前 - … of lung X-ray images. This paper introduces a novel algorithm designed to identify
abnormalities in X-ray … Several methods based on CNN and deep learning techniques have …

4 天前 - … common lung cancer … , a deep neural network model that has demonstrated its
capabilities in various fields. Before being used on the model, the dataset containing lung X-ray …