[HTML][HTML] The importance of resource awareness in artificial intelligence for healthcare
Artificial intelligence and machine learning (AI/ML) models have been adopted in a wide
range of healthcare applications, from medical image computing and analysis to continuous …
range of healthcare applications, from medical image computing and analysis to continuous …
Deep learning in medical imaging: A brief review
S Serte, A Serener, F Al‐Turjman - Transactions on Emerging …, 2022 - Wiley Online Library
Researchers have used deep learning methods for a human level or better disease
identification and detection. This paper reports, in brief, the recent work in deep learning …
identification and detection. This paper reports, in brief, the recent work in deep learning …
[HTML][HTML] High-precision multiclass classification of lung disease through customized MobileNetV2 from chest X-ray images
In this study, multiple lung diseases are diagnosed with the help of the Neural Network
algorithm. Specifically, Emphysema, Infiltration, Mass, Pleural Thickening, Pneumonia …
algorithm. Specifically, Emphysema, Infiltration, Mass, Pleural Thickening, Pneumonia …
Models genesis
Transfer learning from natural images to medical images has been established as one of the
most practical paradigms in deep learning for medical image analysis. To fit this paradigm …
most practical paradigms in deep learning for medical image analysis. To fit this paradigm …
A lightweight CNN-based network on COVID-19 detection using X-ray and CT images
ML Huang, YC Liao - Computers in Biology and Medicine, 2022 - Elsevier
Background and objectives The traditional method of detecting COVID-19 disease mainly
rely on the interpretation of computer tomography (CT) or X-ray images (X-ray) by doctors or …
rely on the interpretation of computer tomography (CT) or X-ray images (X-ray) by doctors or …
[HTML][HTML] Comparison of deep learning approaches for multi-label chest X-ray classification
The increased availability of labeled X-ray image archives (eg ChestX-ray14 dataset) has
triggered a growing interest in deep learning techniques. To provide better insight into the …
triggered a growing interest in deep learning techniques. To provide better insight into the …
Delving into masked autoencoders for multi-label thorax disease classification
Abstract Vision Transformer (ViT) has become one of the most popular neural architectures
due to its simplicity, scalability, and compelling performance in multiple vision tasks …
due to its simplicity, scalability, and compelling performance in multiple vision tasks …
Simplified transfer learning for chest radiography models using less data
Background Developing deep learning models for radiology requires large data sets and
substantial computational resources. Data set size limitations can be further exacerbated by …
substantial computational resources. Data set size limitations can be further exacerbated by …
Triple attention learning for classification of 14 thoracic diseases using chest radiography
Chest X-ray is the most common radiology examinations for the diagnosis of thoracic
diseases. However, due to the complexity of pathological abnormalities and lack of detailed …
diseases. However, due to the complexity of pathological abnormalities and lack of detailed …
[HTML][HTML] Segmentation and classification on chest radiography: a systematic survey
T Agrawal, P Choudhary - The Visual Computer, 2023 - Springer
Chest radiography (X-ray) is the most common diagnostic method for pulmonary disorders.
A trained radiologist is required for interpreting the radiographs. But sometimes, even …
A trained radiologist is required for interpreting the radiographs. But sometimes, even …