Article
Computer Science, Artificial Intelligence
Siyuan Lu, Shui-Hua Wang, Yu-Dong Zhang
Summary: This paper proposed a novel method for abnormal brain detection in magnetic resonance images, which modified AlexNet and utilized extreme learning machine for training and optimization, achieving state-of-the-art performance.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Biology
Xinxin Yang, Mark Stamp
Summary: Low grade endometrial stromal sarcoma (LGESS) is a rare type of uterine cancer, and classic machine learning and deep learning models can be used to assist in its diagnosis. The research shows that deep learning models have a slightly higher classification accuracy compared to classic techniques.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Computer Science, Information Systems
Mohammad Alsmirat, Nusaiba Al-Mnayyis, Mahmoud Al-Ayyoub, Asma'a Al-Mnayyis
Summary: This paper presents two convolutional neural network-based computer-aided diagnosis systems for diagnosing lumbar disk herniation from MRI axial scans. The systems achieve high accuracy through the use of deep learning techniques and innovative approaches, including data augmentation and transfer learning.
Article
Oncology
Huan Zheng, Zebin Xiao, Siwei Luo, Suqing Wu, Chuxin Huang, Tingting Hong, Yan He, Yanhui Guo, Guoqing Du
Summary: This study develops a computer aided diagnosis (CAD) system using deep learning to assist radiologists in diagnosing follicular thyroid carcinoma (FTC) on thyroid ultrasonography. The CAD system achieves better performance than radiologists and significantly improves their diagnosis of FTC. It provides a reliable reference for preoperative diagnosis of FTC and may assist in the development of a fast, accessible screening method for FTC.
FRONTIERS IN ONCOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Alvaro S. Hervella, Jose Rouco, Jorge Novo, Marcos Ortega
Summary: This study proposes a self-supervised learning method using unlabeled multimodal data to enhance the accuracy of retinal computer-aided diagnosis systems, without relying on manual annotation. Experimental results demonstrate satisfactory performance in diagnosing different ocular diseases, showcasing the potential of leveraging unlabeled multimodal visual data in the medical field.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Biology
Ronglin Gong, Jing Shi, Jian Wang, Jun Wang, Jianwei Zhou, Xiaofeng Lu, Jun Du, Jun Shi
Summary: This paper proposes an improved self-supervised learning framework HBTN, which utilizes an image restoration task to extract features, embeds class label information to enhance discriminative ability, and integrates the pre-training network and classification network to collaboratively transfer knowledge and improve the performance of CAD models.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Computer Science, Interdisciplinary Applications
Chengze Zhu, Pingge Hu, Xingtong Wang, Xianxu Zeng, Li Shi
Summary: This study developed a deep learning-based CAD method that can recognize hydrops lesions in real-time, providing a robust and accurate solution with powerful feature extraction and segmentation capabilities for auxiliary diagnosis of hydatidiform mole.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
David J. Winkel, Angela Tong, Bin Lou, Ali Kamen, Dorin Comaniciu, Jonathan A. Disselhorst, Alejandro Rodriguez-Ruiz, Henkjan Huisman, Dieter Szolar, Ivan Shabunin, Moon Hyung Choi, Pengyi Xing, Tobias Penzkofer, Robert Grimm, Heinrich von Busch, Daniel T. Boll
Summary: The study evaluated the impact of a deep learning-based DL-CAD system on radiologists' accuracy and efficiency in reading prostate MRI scans. The DL-CAD system improved diagnostic accuracy, reduced variability, and decreased reading time for radiologists.
INVESTIGATIVE RADIOLOGY
(2021)
Article
Computer Science, Hardware & Architecture
Mahmoud Ragab, Wajdi H. Aljedaibi, Alaa F. Nahhas, Ibrahim R. Alzahrani
Summary: This paper presents a deep learning-based computer aided diagnosis model for diabetic retinopathy detection and grading. The model includes preprocessing, image segmentation, feature extraction, and classification, and achieves high accuracy according to the performance validation on benchmark data set.
COMPUTERS & ELECTRICAL ENGINEERING
(2022)
Article
Computer Science, Interdisciplinary Applications
Yongwon Cho, Yeo Eun Han, Min Ju Kim, Beom Jin Park, Ki Choon Sim, Deuk Jae Sung, Na Yeon Han, Yang Shin Park
Summary: In this study, a computer-aided detection (CAD) system for hepatocellular carcinoma (HCC) on gadoxetic acid-enhanced MRI was developed using a convolutional neural network (CNN). The feasibility of the CAD was evaluated on multi-sequence, multi-unit, and multi-center datasets.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Zhaonan Sun, Kexin Wang, Zixuan Kong, Zhangli Xing, Yuntian Chen, Ning Luo, Yang Yu, Bin Song, Pengsheng Wu, Xiangpeng Wang, Xiaodong Zhang, Xiaoying Wang
Summary: This study compares the performance of radiologists in detecting MRI-visible clinically significant prostate cancer (csPCa) with and without AI-based software. The results show that the use of AI software improves the sensitivity and specificity of radiologists, reduces reading time, and increases diagnostic confidence.
INSIGHTS INTO IMAGING
(2023)
Article
Public, Environmental & Occupational Health
Junbo Xuan, Baoyi Ke, Wenyu Ma, Yinghao Liang, Wei Hu
Summary: This study investigates the application of artificial intelligence technology in the auxiliary diagnosis of spinal diseases. By labeling the MRIs of 604 patients using the LableImg tool, different deep transfer learning models were created and trained. The results showed that the PP-YOLOv2 model achieved a 90.08% overall accuracy in the diagnosis of normal, IVD bulges, and spondylolisthesis.
FRONTIERS IN PUBLIC HEALTH
(2023)
Article
Chemistry, Multidisciplinary
Haojun Qin, Lei Zhang, Quan Guo
Summary: This study developed a computer-aided diagnostic system based on deep learning to classify benign and malignant tumors in breast ultrasound images from paper reports. The proposed method achieved an accuracy of 89.31%, recall rate of 88.65%, specificity of 89.57%, F1 score of 89.42%, and AUC of 94.53% when the input images contained noise. This approach is more suitable for practical applications and can assist patients in obtaining prompt and accurate classification results of ultrasound reports.
APPLIED SCIENCES-BASEL
(2023)
Article
Engineering, Electrical & Electronic
Sreedhar Kollem, Katta Ramalinga Reddy, Ch. Rajendra Prasad, Avishek Chakraborty, J. Ajayan, S. Sreejith, Sandip Bhattacharya, L. M. I. Leo Joseph, Ravichander Janapati
Summary: Deep learning is often used for medical image classification, allowing surgeons to determine tumor types before performing surgery. To address overfitting in classification due to insufficient training samples, a new deep learning methodology is proposed. This method combines a unique data augmentation model, modified AlexNet, and network-based deep transfer learning to categorize MRI brain tumor images.
INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY
(2023)
Article
Automation & Control Systems
G. Chakrapani, V. Sugumaran
Summary: This study uses deep learning technique (transfer learning) to diagnose faults in dry friction clutches. By analyzing vibration signals and utilizing pre-trained network models, accurate diagnosis of various clutch faults is achieved.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Chaosheng Tang, Bin Li, Junding Sun, Shui-Hua Wang, Yu-Dong Zhang
Summary: Brain tumor is a common disease of the central nervous system, which has high morbidity and mortality. This paper proposes SpCaNet, a lightweight and efficient model for recognizing brain tumors, using a combination of Positional Attention (PA) convolution block, Relative self-attention transformer block, and Intermittent fully connected (IFC) layer. The model has achieved the highest accuracy of 99.28% in classifying brain tumors, outperforming the state-of-the-art model.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Xiang Yu, Shui-Hua Wang, Yu-Dong Zhang
Summary: To facilitate faster breast cancer detection, a novel and efficient patch-based breast mass detection system was developed. The system consists of three modules: pre-processing, multiple-level breast tissue segmentation, and final breast mass detection. An improved Deeplabv3+ model is used for pre-processing and a multiple-level thresholding segmentation method is proposed for breast mass segmentation. Deep learning models are trained to classify image patches into breast mass or background, reducing the false positive rate. The proposed method achieves comparable performance with state-of-the-art methods.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2023)
Review
Imaging Science & Photographic Technology
Xue Han, Zuojin Hu, Shuihua Wang, Yudong Zhang
Summary: According to the World Health Organization, there have been 625,248,843 confirmed cases of COVID-19, including 65,622,281 deaths worldwide as of October 25, 2022. The diagnosis of COVID-19 using chest X-ray or CT images based on convolutional neural networks (CNN) is an important method for reducing misdiagnosis. This paper introduces the latest deep learning methods and techniques for COVID-19 diagnosis and analyzes existing CNN automatic diagnosis systems, concluding that CNN has essential value in COVID-19 diagnosis and can be further improved with expanded datasets and advanced techniques.
JOURNAL OF IMAGING
(2023)
Article
Computer Science, Artificial Intelligence
Xing Guo, Siyuan Lu, Shuihua Wang, Zhihai Lu, Yudong Zhang
Summary: This paper proposes a facial expression recognition method based on a double-code LBP-layer spatial-attention network (DLSANet) to improve the accuracy of FER. The DLSANet achieves recognition accuracies of 93.81% and 98.68% on the JAFFE and CK+ datasets, respectively, outperforming state-of-the-art methods.
IET IMAGE PROCESSING
(2023)
Article
Computer Science, Information Systems
Xiang Yu, Ziquan Zhu, Yoav Alon, David S. S. Guttery, Yudong Zhang
Summary: GFNet is a novel framework for detecting breast masses, consisting of three modules: patch extraction, feature extraction, and mass detection. It is highly robust and adaptable to images from different devices. The proposed method enhances the information of breast masses with gradient field convergence features, and reduces false positives through combining texture and morphological features. Experimental results demonstrate that GFNet outperforms other methods on two datasets.
Article
Computer Science, Information Systems
Shuwen Chen, Siyuan Lu, Shuihua Wang, Yiyang Ni, Yudong Zhang
Summary: Blood cells play a vital role in human metabolism and their status can be used for clinical diagnoses. Manual analysis of blood cell classification is time-consuming, but recent advancements in computer vision can free doctors from this tedious task. This paper proposes a novel automated blood cell classification model, SW-ViT, which outperforms state-of-the-art methods in terms of classification accuracy. The proposed SW-ViT can be applied in daily clinical diagnosis.
Article
Computer Science, Artificial Intelligence
Navid Ghassemi, Afshin Shoeibi, Marjane Khodatars, Jonathan Heras, Alireza Rahimi, Assef Zare, Yu-Dong Zhang, Ram Bilas Pachori, Manuel Gorriz
Summary: The outbreak of COVID-19 has had a significant impact on people worldwide. Accurately diagnosing and isolating patients is crucial in fighting this pandemic, and medical imaging, particularly CT imaging, has been a focus of research due to its accuracy and availability. This paper presents a method using pre-trained deep neural networks and a CycleGAN model for data augmentation, achieving state-of-the-art performance with 99.60% accuracy. A dataset of 3163 images from 189 patients, collected from suspected COVID-19 cases, has been publicly made available for evaluation. The method's reliability is further assessed using calibration metrics and the Grad-CAM technique for explaining its decisions.
APPLIED SOFT COMPUTING
(2023)
Editorial Material
Automation & Control Systems
Jerry Chun-Wei Lin, Gautam Srivastava, Yudong Zhang
ASIAN JOURNAL OF CONTROL
(2023)
Review
Computer Science, Artificial Intelligence
M. Tanveer, M. A. Ganaie, Iman Beheshti, Tripti Goel, Nehal Ahmad, Kuan-Ting Lai, Kaizhu Huang, Yu-Dong Zhang, Javier Del Ser, Chin-Teng Lin
Summary: Over the years, Machine Learning models have been successfully used for predicting brain age accurately based on neuroimaging data. This review comprehensively analyzes the adoption of deep learning for brain age estimation and explores different deep learning architectures and frameworks used in this field. The paper aims to establish a common reference for newcomers and experienced researchers interested in utilizing deep learning models for brain age estimation.
INFORMATION FUSION
(2023)
Article
Computer Science, Artificial Intelligence
Yudong Zhang, Lijia Deng, Hengde Zhu, Wei Wang, Zeyu Ren, Qinghua Zhou, Siyuan Lu, Shiting Sun, Ziquan Zhu, Juan Manuel Gorriz, Shuihua Wang
Summary: Integrating artificial intelligence with food category recognition has been a field of interest for research, and it has the potential to revolutionize human interaction with food. The advancements in big data and deep learning have provided better recognition methods. This survey focuses on machine learning systems for food category recognition, including datasets, data augmentation, feature extraction, and algorithms, with a special emphasis on deep learning techniques.
INFORMATION FUSION
(2023)
Article
Computer Science, Information Systems
Xiang Yu, Zeyu Ren, David S. Guttery, Yu-Dong Zhang
Summary: Breast cancer is a common and serious health threat in the UK. Early detection is crucial for effective treatment. Image-based methods, such as mammography, offer less invasive and time-consuming alternatives to biopsy. Our study developed a novel breast mass classification system called DF-dRVFL, which showed promising results in classifying breast masses with high accuracy and efficiency.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Review
Computer Science, Information Systems
Xing Guo, Yudong Zhang, Siyuan Lu, Zhihai Lu
Summary: This study summarizes the methods and datasets of facial expression recognition, including machine learning and deep learning methods, and compares their advantages and limitations. It also concludes the current problems and future development of facial expression recognition.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Review
Computer Science, Artificial Intelligence
Zeyu Ren, Shuihua Wang, Yudong Zhang
Summary: Supervised learning aims to establish multiple mappings between training data and outputs through building a function or model, while weakly supervised learning is more applicable for medical image analysis due to the lack of sufficient labels. This review provides an overview of the latest progress in weakly supervised learning for medical image analysis, including incomplete, inexact, and inaccurate supervision, as well as introduces related works on different applications. Challenges and future developments of weakly supervised learning in medical image analysis are also discussed.
CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY
(2023)
Article
Biology
Wei Wang, Yanrong Pei, Shui-Hua Wang, Juan Manuel Gorrz, Yu-Dong Zhang
Summary: Since 2019, the COVID-19 pandemic has posed a significant threat to the global economy and human health. Deep learning-based computer-aided diagnosis models can effectively alleviate the challenges of diagnosing COVID-19 due to limited healthcare resources. To overcome the time-consuming and unstable nature of traditional hyperparameter tuning methods, we propose a Particle Swarm Optimization-guided Self-Tuning Convolution Neural Network (PSTCNN) that automatically adjusts the model's hyperparameters.
Article
Oncology
Ziquan Zhu, Shui-Hua Wang, Yu-Dong Zhang
Summary: An automated network model for blood cell classification is proposed, which can assist doctors in diagnosing disease types and severity. The model uses a ResNet50 backbone for feature extraction and applies an ensemble of three randomized neural networks based on majority voting. Experimental results show that the proposed method outperforms other state-of-the-art methods in terms of classification performance.
TECHNOLOGY IN CANCER RESEARCH & TREATMENT
(2023)
