Artificial intelligence in oncology

H Shimizu, KI Nakayama - Cancer science, 2020 - Wiley Online Library
Artificial intelligence (AI) has contributed substantially to the resolution of a variety of
biomedical problems, including cancer, over the past decade. Deep learning, a subfield of AI …

Convolutional neural networks or vision transformers: Who will win the race for action recognitions in visual data?

O Moutik, H Sekkat, S Tigani, A Chehri, R Saadane… - Sensors, 2023 - mdpi.com
Understanding actions in videos remains a significant challenge in computer vision, which
has been the subject of several pieces of research in the last decades. Convolutional neural …

Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning

R Desislavov, F Martínez-Plumed… - … Informatics and Systems, 2023 - Elsevier
The progress of some AI paradigms such as deep learning is said to be linked to an
exponential growth in the number of parameters. There are many studies corroborating …

A novel transfer learning based approach for pneumonia detection in chest X-ray images

V Chouhan, SK Singh, A Khamparia, D Gupta… - Applied Sciences, 2020 - mdpi.com
Pneumonia is among the top diseases which cause most of the deaths all over the world.
Virus, bacteria and fungi can all cause pneumonia. However, it is difficult to judge the …

Diagnosis of monkeypox disease using transfer learning and binary advanced dipper throated optimization algorithm

AH Alharbi, SK Towfek, AA Abdelhamid, A Ibrahim… - Biomimetics, 2023 - mdpi.com
The virus that causes monkeypox has been observed in Africa for several years, and it has
been linked to the development of skin lesions. Public panic and anxiety have resulted from …

[HTML][HTML] Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images

S Rajaraman, SK Antani, M Poostchi, K Silamut… - PeerJ, 2018 - peerj.com
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 …

Convolutional neural networks for medical image analysis: Full training or fine tuning?

N Tajbakhsh, JY Shin, SR Gurudu… - IEEE transactions on …, 2016 - ieeexplore.ieee.org
Training a deep convolutional neural network (CNN) from scratch is difficult because it
requires a large amount of labeled training data and a great deal of expertise to ensure …

Convolutional neural network based fault detection for rotating machinery

O Janssens, V Slavkovikj, B Vervisch… - Journal of Sound and …, 2016 - Elsevier
Vibration analysis is a well-established technique for condition monitoring of rotating
machines as the vibration patterns differ depending on the fault or machine condition …

Striving for simplicity: The all convolutional net

JT Springenberg, A Dosovitskiy, T Brox… - arXiv preprint arXiv …, 2014 - arxiv.org
Most modern convolutional neural networks (CNNs) used for object recognition are built
using the same principles: Alternating convolution and max-pooling layers followed by a …

Artificial intelligence in fracture detection: transfer learning from deep convolutional neural networks

DH Kim, T MacKinnon - Clinical radiology, 2018 - Elsevier
Aim To identify the extent to which transfer learning from deep convolutional neural networks
(CNNs), pre-trained on non-medical images, can be used for automated fracture detection …