Results 11 to 20 of about 28,970 (210)

GAN‐LSTM‐3D: An efficient method for lung tumour 3D reconstruction enhanced by attention‐based LSTM

CAAI Transactions on Intelligence Technology, EarlyView., 2023
Abstract Three‐dimensional (3D) image reconstruction of tumours can visualise their structures with precision and high resolution. In this article, GAN‐LSTM‐3D method is proposed for 3D reconstruction of lung cancer tumours from 2D CT images. Our method consists of three phases: lung segmentation, tumour segmentation, and tumour 3D reconstruction. Lung
Lu Hong, Mohammad Hossein Modirrousta, Mohammad Hossein Nasirpour, Mohammadreza Mirshekari Chargari, Fardin Mohammadi, Seyed Vahid Moravvej, Leila Rezvanishad, Mohammadreza Rezvanishad, Ivan Bakhshayeshi, Roohallah Alizadehsani, Imran Razzak, Hamid Alinejad‐Rokny, Saeid Nahavandi   +12 more
wiley   +1 more source

An efficient deep learning model for brain tumour detection with privacy preservation

CAAI Transactions on Intelligence Technology, EarlyView., 2023
Abstract Internet of medical things (IoMT) is becoming more prevalent in healthcare applications as a result of current AI advancements, helping to improve our quality of life and ensure a sustainable health system. IoMT systems with cutting‐edge scientific capabilities are capable of detecting, transmitting, learning and reasoning.
Mujeeb Ur Rehman, Arslan Shafique, Imdad Ullah Khan, Yazeed Yasin Ghadi, Jawad Ahmad, Mohammed S. Alshehri, Mimonah Al Qathrady, Majed Alhaisoni, Muhammad H. Zayyan   +8 more
wiley   +1 more source

Classification of Pancreatic Cystic Tumors Based on DenseNet and Transfer Learning

Chinese Journal of Magnetic Resonance, 2023
This work applied the classification model of DenseNet combined with transfer learning to classify mucinous cystic tumor (MCN) from serous cystic tumor (SCN) of the pancreas.
TIAN Hui, Wu Jie, BIAN Yun, ZHANG Zhiwei, SHAO Chengwei   +4 more
doaj   +1 more source

Galaxy Morphology Classification with DenseNet

Journal of Physics: Conference Series, 2022
Abstract Galaxy classification is crucial in astronomy, as galaxy types reveal information on how the galaxy was formed and evolved. While manually conducting the classification task requires extensive background knowledge and is time-consuming, deep learning algorithms provide a time-efficient and expedient way of accomplishing this ...
Wuyu Hui, Zheng Robert Jia, Hansheng Li, Zijian Wang   +3 more
openaire   +1 more source

A hybrid attention-enhanced DenseNet neural network model based on improved U-Net for rice leaf disease identification

Frontiers in Plant Science, 2022
Rice is a necessity for billions of people in the world, and rice disease control has been a major focus of research in the agricultural field. In this study, a new attention-enhanced DenseNet neural network model is proposed, which includes a lesion ...
Wufeng Liu, Wufeng Liu, Liang Yu, Jiaxin Luo   +3 more
doaj   +1 more source

A Novel Weight-Shared Multi-Stage CNN for Scale Robustness [PDF]

, 2019
Convolutional neural networks (CNNs) have demonstrated remarkable results in image classification for benchmark tasks and practical applications. The CNNs with deeper architectures have achieved even higher performance recently thanks to their robustness
Matsubara, Takashi, Takahashi, Ryo, Uehara, Kuniaki   +2 more
core   +2 more sources

Invertible DenseNets

, 2020
Accepted at 3rd Symposium on Advances in Approximate Bayesian Inference (AABI)
Perugachi-Diaz, Yura, Tomczak, Jakub M., Bhulai, Sandjai   +2 more
openaire   +2 more sources

Spinal Cord Segmentation in Ultrasound Medical Imagery

Applied Sciences, 2020
In this paper, we study and evaluate the task of semantic segmentation of the spinal cord in ultrasound medical imagery. This task is useful for neurosurgeons to analyze the spinal cord movement during and after the laminectomy surgical operation ...
Bilel Benjdira, Kais Ouni, Mohamad M. Al Rahhal, Abdulrahman Albakr, Amro Al-Habib, Emad Mahrous   +5 more
doaj   +1 more source

DenseNet for dense flow

2017 IEEE International Conference on Image Processing (ICIP), 2017
Classical approaches for estimating optical flow have achieved rapid progress in the last decade. However, most of them are too slow to be applied in real-time video analysis. Due to the great success of deep learning, recent work has focused on using CNNs to solve such dense prediction problems.
Zhu, Yi, Newsam, Shawn
openaire   +2 more sources

Detecting Abnormal Driving Behavior Using Modified DenseNet

Iraqi Journal for Computer Science and Mathematics, 2023
Car accidents have serious consequences, including depletion of resources, harm to human health and well-being, and social problems. The three primary factors contributing to car accidents are driver error, external factors, and vehicle-related factors.
Aisha Ayad, Matheel E. Abdulmunim
doaj   +1 more source