How far have we come? Artificial intelligence for chest radiograph interpretation
Due to recent advances in artificial intelligence, there is renewed interest in automating
interpretation of imaging tests. Chest radiographs are particularly interesting due to many …
interpretation of imaging tests. Chest radiographs are particularly interesting due to many …
[HTML][HTML] Key concepts, common pitfalls, and best practices in artificial intelligence and machine learning: focus on radiomics
B Koçak - Diagnostic and Interventional Radiology, 2022 - ncbi.nlm.nih.gov
Artificial intelligence (AI) and machine learning (ML) are increasingly used in radiology
research to deal with large and complex imaging data sets. Nowadays, ML tools have …
research to deal with large and complex imaging data sets. Nowadays, ML tools have …
[HTML][HTML] Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images
Malaria is a blood disease caused by the Plasmodium parasites transmitted through the bite
of female Anopheles mosquito. Microscopists commonly examine thick and thin blood …
of female Anopheles mosquito. Microscopists commonly examine thick and thin blood …
Malaria disease detection using cnn technique with sgd, rmsprop and adam optimizers
Malaria is life-threatening disease spread when an infected female Anopheles mosquito
bites a person. Malaria is one of the predominant diseases in the world. There exists many …
bites a person. Malaria is one of the predominant diseases in the world. There exists many …
Detection and classification of marine mammal sounds using AlexNet with transfer learning
T Lu, B Han, F Yu - Ecological Informatics, 2021 - Elsevier
In this study, AlexNet with transfer learning was employed to automatically detect and
classify the sounds of killer whales, long-finned pilot whales, and harp seals with widely …
classify the sounds of killer whales, long-finned pilot whales, and harp seals with widely …
RETRACTED ARTICLE: Deep CNN framework for retinal disease diagnosis using optical coherence tomography images
N Rajagopalan, V Narasimhan… - Journal of Ambient …, 2021 - Springer
Accurate and robust diagnosis of retinal diseases using OCT imaging is considered an
essential part for clinical utility. We propose a deep learning based, a fully automated …
essential part for clinical utility. We propose a deep learning based, a fully automated …
A novel stacked model ensemble for improved TB detection in chest radiographs
Tuberculosis (TB) is an airborne infection and a common cause of deaths related to
antimicrobial resistance. Under-reporting of the disease and inadequate care for controlling …
antimicrobial resistance. Under-reporting of the disease and inadequate care for controlling …
[PDF][PDF] Models of learning to classify X-ray images for the detection of pneumonia using neural networks.
This article describes a comparison of two neural networks, the multilayer perceptron and
Neural Network, for the detection and classification of pneumonia. The database used was …
Neural Network, for the detection and classification of pneumonia. The database used was …
Malaria parasite detection using deep learning:(Beneficial to humankind)
D Shah, K Kawale, M Shah, S Randive… - 2020 4th International …, 2020 - ieeexplore.ieee.org
Malaria is one of the deadliest diseases across the globe. This is caused by the bite of
female Anopheles mosquito that transmits the Plasmodium parasites. Some current malaria …
female Anopheles mosquito that transmits the Plasmodium parasites. Some current malaria …
A generic intelligent bearing fault diagnosis system using convolutional neural networks with transfer learning
T Lu, F Yu, B Han, J Wang - Ieee Access, 2020 - ieeexplore.ieee.org
It is very important and necessary to diagnose bearing faults timely, quickly, and accurately
in practical applications, because the operation status of the bearings is directly related to …
in practical applications, because the operation status of the bearings is directly related to …