Article
Computer Science, Artificial Intelligence
Lin Zhang, Lei Ren, Tian Bai, Mingyu Du, Li Ruan, Yuan Yang, Guanghao Qian, Zihao Meng, Li Zhao, M. Jamal Deen
Summary: The paper proposes a novel AD detection method based on an adversarial network, which combines generative adversarial network (GAN) and convolutional neural network (CNN) to extract high-level brain features for AD diagnosis. Experimental results demonstrate that this method extracts more representative brain features and achieves a significant improvement in diagnosis performance.
Article
Automation & Control Systems
Pakize Erdogmus, Abdullah Talha Kabakus
Summary: Alzheimer's Disease is a devastating neurologic disorder with no cure, and its symptoms eventually interfere with daily tasks. We propose a novel Convolutional Neural Network as a cheap, fast, yet accurate solution for early diagnosis, achieving an accuracy of 90.4% which outperforms existing classifiers.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
R. S. Nancy Noella, J. Priyadarshini
Summary: Dementia is a brain-related issue that affects memory, and it severely impacts thinking and daily routines. Previous studies focused on diagnosing a single type of dementia, but this research proposes a system that can diagnose multiple types using FDG-PET brain scans. The system employs a generative adversarial deep convolutional neural network to generate virtual samples and address the issue of uneven distribution in the dataset. The model achieves an overall accuracy of 97.7%.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Medicine, General & Internal
Feyisope R. Eweje, Bingting Bao, Jing Wu, Deepa Dalal, Wei-hua Liao, Yu He, Yongheng Luo, Shaolei Lu, Paul Zhang, Xianjing Peng, Ronnie Sebro, Harrison X. Bai, Lisa States
Summary: A deep learning algorithm was developed in this study to differentiate benign and malignant bone lesions using MRI images and patient demographics, achieving performance comparable to radiology experts. These findings could aid in the development of computer-aided diagnostic tools to reduce unnecessary referrals to specialized centers and biopsies.
Article
Oncology
Mubashar Mehmood, Sadam Hussain Abbasi, Khursheed Aurangzeb, Muhammad Faran Majeed, Muhammad Shahid Anwar, Musaed Alhussein
Summary: This study proposes a methodology combining deep learning and transfer learning for identifying prostate cancer using MRI images. By utilizing the EfficientNet architecture and three branches for feature extraction, the model achieves remarkable accuracy of 88.89% in classifying prostate cancer.
FRONTIERS IN ONCOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Ram Krishn Mishra, Siddhaling Urolagin, J. Angel Arul Jothi, Pramod Gaur
Summary: Image processing is a technique used to apply various operations to images to improve them or extract information, with facial recognition being a prominent application. This study examines the accuracy of categorizing human facial expressions using deep learning and transfer learning methods, proposing a deep hybrid learning approach that combines multiple deep learning models.
IMAGE AND VISION COMPUTING
(2022)
Article
Computer Science, Interdisciplinary Applications
Zhentao Hu, Zheng Wang, Yong Jin, Wei Hou
Summary: This study proposes a VGG-TSwinformer model based on convolutional neural network (CNN) and Transformer for short-term longitudinal study of MCI. This model utilizes VGG-16 based CNN to extract low-level spatial features of longitudinal sMRI images and maps them to high-level feature representations. Sliding-window attention is used for fine-grained fusion of spatially adjacent feature representations, and temporal attention is used to measure the evolution of these feature representations. The model achieved high accuracy, sensitivity, specificity, and AUC in the classification task of sMCI vs pMCI on the ADNI dataset.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2023)
Article
Multidisciplinary Sciences
Sam Gelman, Sarah A. Fahlberg, Pete Heinzelman, Philip A. Romero, Anthony Gitter
Summary: This study presents a supervised deep learning framework for mapping protein sequence to function, demonstrating superior performance in predicting the behavior of protein sequence variants. Analysis of the trained models highlights the importance of capturing nonlinear interactions and parameter sharing in neural networks for learning the relationship between sequence and function. Additionally, the research shows the networks' ability to learn biologically meaningful information about protein structure and mechanism, as well as design new proteins beyond the training set.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2021)
Article
Computer Science, Artificial Intelligence
Matthew A. Haber, Giorgio P. Biondetti, Romane Gauriau, Donnella S. Comeau, John K. Chin, Bernardo C. Bizzo, Julia Strout, Alexandra J. Golby, Katherine P. Andriole
Summary: In this study, a convolutional neural network (CNN) was developed to detect idiopathic normal pressure hydrocephalus (iNPH) on head CT scans. The CNN showed high sensitivity and specificity in identifying iNPH, suggesting its potential as a screening tool in clinical practice, particularly in high-volume settings.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Gao Huang, Zhuang Liu, Geoff Pleiss, Laurens van der Maaten, Kilian Q. Weinberger
Summary: Recent work has shown that adding shorter connections in convolutional networks can make the network deeper, more accurate, and more efficient in training. This paper introduces Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward manner. DenseNets alleviate the vanishing-gradient problem, encourage feature reuse, and improve parameter efficiency, leading to significant improvements in object recognition tasks.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Biology
Yeshe M. Kway, Kashthuri Thirumurugan, Navin Michael, Kok Hian Tan, Keith M. Godfrey, Peter Gluckman, Yap Seng Chong, Kavita Venkataraman, Eric Yin Hao Khoo, Chin Meng Khoo, Melvin Khee-Shing Leow, E. Shyong Tai, Jerry KY. Chan, Shiao-Yng Chan, Johan G. Eriksson, Marielle Fortier, Yung Seng Lee, S. Sendhil Velan, Mengling Feng, Suresh Anand Sadananthan
Summary: By using a convolutional neural network to segment abdominal adipose tissue, it is possible to accurately divide subcutaneous adipose tissue (SAT) into superficial and deep subgroups, and internal adipose tissue (IAT) into intraperitoneal, retroperitoneal, and paraspinal subgroups.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Chemistry, Multidisciplinary
Haitham Alsaif, Ramzi Guesmi, Badr M. Alshammari, Tarek Hamrouni, Tawfik Guesmi, Ahmed Alzamil, Lamia Belguesmi
Summary: Brain tumor is a severe cancer that requires early detection for effective treatment. Recent progress in deep learning, specifically convolutional neural networks (CNNs), has greatly contributed to the medical diagnosis of brain tumors using MRI images. However, there is a need to improve deep learning algorithms and CNNs to enhance efficiency due to limited datasets. This paper provides a detailed review of various CNN architectures and proposes an efficient method for brain tumor detection using MRI datasets based on CNN and data augmentation.
APPLIED SCIENCES-BASEL
(2022)
Article
Clinical Neurology
Malte Klingenberg, Didem Stark, Fabian Eitel, Celine Budding, Mohamad Habes, Kerstin Ritter, Alzheimers Dis Neuroimaging Initiat
Summary: This study trained a convolutional neural network using a balanced dataset to detect Alzheimer's disease. The results showed that the machine learning classifier had different performance for men and women, indicating the presence of sex bias. The findings emphasize the importance of examining and reporting classifier performance across population subgroups to ensure algorithmic fairness.
ALZHEIMERS RESEARCH & THERAPY
(2023)
Article
Computer Science, Artificial Intelligence
Zhuangzhi Chen, Jingyang Xiang, Yao Lu, Qi Xuan, Zhen Wang, Guanrong Chen, Xiaoniu Yang
Summary: This article studies the graph structure of the neural network and proposes a regular graph pruning (RGP) method to achieve one-shot neural network pruning. The experiments show that the average shortest path-length (ASPL) of the graph is negatively correlated with the classification accuracy of the neural network, and RGP has a strong precision retention capability with high parameter and FLOPs reduction.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Medicine, General & Internal
Daphne Mulliez, Edouard Poncelet, Laurie Ferret, Christine Hoeffel, Blandine Hamet, Lan Anh Dang, Nicolas Laurent, Guillaume Ramette
Summary: The aim of this study was to develop a deep learning tool for automated measurement of the three-dimensional size of the uterus on MRI. A CNN model was trained and tested, which showed good performance in locating the uterus and obtaining its three-dimensional measurement compared to manual measurements.
Article
Endocrinology & Metabolism
Zhou Zhang, Bing Zhang, Xin Wang, Xin Zhang, Qing X. Yang, Zhao Qing, Jiaming Lu, Yan Bi, Dalong Zhu
Article
Radiology, Nuclear Medicine & Medical Imaging
Yongquan Ye, Fei Zhou, Jinguang Zong, Jingyuan Lyu, Yanling Chen, Shuheng Zhang, Weiguo Zhang, Qiang He, Xueping Li, Ming Li, Qinglei Zhang, Zhao Qing, Bing Zhang
JOURNAL OF MAGNETIC RESONANCE IMAGING
(2019)
Article
Endocrinology & Metabolism
Zhou Zhang, Bing Zhang, Xin Wang, Xin Zhang, Qing X. Yang, Zhao Qing, Wen Zhang, Dalong Zhu, Yan Bi
Article
Neurosciences
Hai-Feng Chen, Li-Li Huang, Hui-Ya Li, Yi Qian, Dan Yang, Zhao Qing, Cai-Mei Luo, Meng-Chun Li, Bing Zhang, Yun Xu
CNS NEUROSCIENCE & THERAPEUTICS
(2020)
Article
Neurosciences
Jiaming Lu, Lihua Yuan, Jiaxuan Jin, Shangwen Yang, Wen Zhang, Ming Li, Xin Zhang, Junxia Wang, Sichu Wu, Qian Chen, Zhao Qing, Yutian Dai, Bing Zhang, Zhishun Wang
FRONTIERS IN HUMAN NEUROSCIENCE
(2020)
Article
Behavioral Sciences
Qian Chen, Zhao Qing, Jiaxuan Jin, Yi Sun, Wenqian Chen, Jiaming Lu, Pin Lv, Jiani Liu, Xin Li, Junxia Wang, Wen Zhang, Sichu Wu, Xian Yan, Zuzana Nedelska, Jakub Hort, Xin Zhang, Bing Zhang
Summary: Individuals with subjective cognitive decline (SCD) show deficits in spatial navigation (SN) and reduced brain network connectivity. Logistic regression based on SN and functional connectivity (FC) measures can effectively differentiate SCD individuals from controls.
Article
Geriatrics & Gerontology
Qian Chen, Sichu Wu, Xin Li, Yi Sun, Wenqian Chen, Jiaming Lu, Wen Zhang, Jiani Liu, Zhao Qing, Zuzana Nedelska, Jakub Hort, Xin Zhang, Bing Zhang
Summary: Individuals with subjective cognitive decline (SCD) show impairments in spatial navigation and may have promising indicators for the early detection of incipient Alzheimer's disease (AD), including basal forebrain (BF) atrophy.
FRONTIERS IN AGING NEUROSCIENCE
(2021)
Article
Geriatrics & Gerontology
Qian Chen, Jiaming Lu, Xin Zhang, Yi Sun, Wenqian Chen, Xin Li, Wen Zhang, Zhao Qing, Bing Zhang
Summary: The study identified distinct differences in dynamic functional connectivity (DFC) and static graph theory parameters in individuals with subjective cognitive decline (SCD), which were significantly associated with cognitive performance, providing potential neuroimaging biomarkers for the early detection of Alzheimer's disease.
FRONTIERS IN AGING NEUROSCIENCE
(2021)
Article
Neurosciences
Xin Li, Zhongyuan Wang, Qian Chen, Xiaoyun Wang, Zhao Qing, Wen Zhang, Jiaming Lu, Junxia Wang, Xin Zhang, Jiani Liu, Zhengge Wang, Baoxin Li, Bing Zhang
Summary: This study identified focal atrophy in the basolateral region of the left amygdala in drug-resistant IGE patients, suggesting it may aid in predicting drug responses and identifying potential therapeutic targets.
FRONTIERS IN NEUROSCIENCE
(2021)
Article
Geriatrics & Gerontology
Weiping Li, Hui Zhao, Zhao Qing, Zuzana Nedelska, Sichu Wu, Jiaming Lu, Wenbo Wu, Zhenyu Yin, Jakub Hort, Yun Xu, Bing Zhang
Summary: In this study, impairment in spatial navigation accuracy was observed in patients with mild cognitive impairment (MCI) and was found to be associated with abnormalities in the structural connectivity network, particularly in the frontal and parietal gyri. These findings provide new insights into the brain mechanisms related to spatial navigation impairment in MCI, independent of white matter hyperintensities (WMH).
FRONTIERS IN AGING NEUROSCIENCE
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Dan Mu, Junjie Bai, Wenping Chen, Hongming Yu, Jing Liang, Kejie Yin, Hui Li, Zhao Qing, Kelei He, Hao-Yu Yang, Jinyao Zhang, Youbing Yin, Hunter W. McLellan, U. Joseph Schoepf, Bing Zhang
Summary: This study developed a deep learning method to automatically quantify coronary artery calcium (CAC) scores from a single coronary CT angiography (CTA) scan. The results of the validation showed that the proposed method had excellent accuracy in quantifying CAC scores and risk categorization when compared to the semiautomatic Agatston scores at noncontrast CT.
Article
Geriatrics & Gerontology
Wen Zhang, Jiaming Lu, Zhao Qing, Xin Zhang, Hui Zhao, Yan Bi, Bing Zhang
Summary: This study found that subcortical structural alterations are related to cognitive decline in patients with type 2 diabetes. However, the impact of type 2 diabetes on Alzheimer's pathology remains unclear.
FRONTIERS IN AGING NEUROSCIENCE
(2022)
Article
Neuroimaging
Xin Zhang, Yu Sun, Weiping Li, Bing Liu, Wenbo Wu, Hui Zhao, Renyuan Liu, Yue Zhang, Zhenyu Yin, Tingting Yu, Zhao Qing, Bin Zhu, Yun Xu, Zuzana Nedelska, Jakub Hort, Bing Zhang
NEUROIMAGE-CLINICAL
(2019)