Article
Computer Science, Artificial Intelligence
Romesh Laishram, Rinku Rabidas
Summary: This paper presents an effective computer-aided detection scheme for automated localization of breast cancer masses. The proposed scheme utilizes a novel contrast enhancement method and a texture-based descriptor to reduce false positives. Experimental results on two standard databases show high sensitivity and low false positive rates.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Biochemical Research Methods
Jiancheng An, Hui Yu, Ru Bai, Jintong Li, Yue Wang, Rui Cao
Summary: This paper proposes a target detection model D-Mask R-CNN based on Mask R-CNN for breast mass detection. Improvements were made to the internal structure of FPN and the size of RPN anchor. Soft-NMS was used to replace NMS for better detection accuracy. Experimental results showed that the improved model achieved a higher mAP value of 0.66 compared to the original Mask R-CNN.
Article
Public, Environmental & Occupational Health
C. Balamou, A. Koivogui, K. Zysman, C. M. Rodrigue, R. Rymzhanova
Summary: This study evaluated the cancer detection rate in the French National Breast Cancer Screening Program. It found that using tomosynthesis technology can improve the cancer detection rate at the first reading session, but may reduce the positive predictive value and cancer detection rate at the second reading session.
Article
Chemistry, Analytical
Madallah Alruwaili, Walaa Gouda
Summary: This study proposes a framework based on transfer learning, utilizing various data augmentation strategies and pre-trained classification networks to distinguish between malignant and benign breast cancer. The system demonstrates high accuracy in experiments, highlighting its feasibility in medical imaging.
Article
Computer Science, Artificial Intelligence
Linlin Zhang, Yanfeng Li, Houjin Chen, Wen Wu, Kuan Chen, Shaokang Wang
Summary: This study proposes an anchor-free YOLOv3 network for mass detection in mammograms, with several improvements, and achieves promising results on two databases.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Review
Computer Science, Information Systems
Nada M. Hassan, Safwat Hamad, Khaled Mahar
Summary: This survey presents a structured overview of current deep learning and traditional machine learning based CAD systems for breast cancer detection and classification. It provides information about publicly available mammographic datasets and evaluation metrics, and discusses the pros, limitations, challenges and limitations in the current literature.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
U. Raghavendra, Anjan Gudigar, Edward J. Ciaccio, Kwan Hoong Ng, Wai Yee Chan, Kartini Rahmat, U. Rajendra Acharya
Summary: Accurate and early detection of breast cancer using effective imaging modalities, such as CAD and BIRADS, is crucial in maintaining patient health. The comparison between 2DSM and FFDM imaging modalities can assist in discriminating BIRADS grades and improving the early detection of breast cancer.
Article
Medicine, General & Internal
Kittipol Wisaeng
Summary: This study proposes a new breast cancer detection method based on K-means++ clustering and Cuckoo Search Optimization. By improving the preprocessing and using mathematical morphology, the accuracy and interpretability of the detection are enhanced. Experimental results show that the method achieves an accuracy of over 95% on three datasets, demonstrating its effectiveness.
Article
Computer Science, Interdisciplinary Applications
Sunita Sarangi, Nrusingha Prasad Rath, Harish Kumar Sahoo
Summary: The research focuses on developing a breast mammogram mass segmentation model using Legendre neural network trained with the BBNSSLMS algorithm. The proposed model achieved a sensitivity of 95% and accuracy of 96% through training with 30 images and testing with 151 images from the MIAS database.
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Jiale Jiang, Junchuan Peng, Chuting Hu, Wenjing Jian, Xianming Wang, Weixiang Liu
Summary: In this paper, a three-stage deep learning framework based on an anchor-free object detection algorithm was proposed to automatically detect and classify breast lesions in mammograms, improving the diagnostic efficiency of radiologists.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2022)
Article
Computer Science, Artificial Intelligence
Wenwei Zhao, Meng Lou, Yunliang Qi, Yiming Wang, Chunbo Xu, Xiangyu Deng, Yide Ma
Summary: Breast cancer is the second most fatal cancer in women, but timely diagnosis and treatment can reduce mortality. Accurate segmentation of breast masses in full-field mammograms is challenging due to low signal-to-noise ratio and uncertainty regarding the mass's shape, size, and location. A novel adaptive channel and multiscale spatial context network is proposed in this study to address this issue and achieve state-of-the-art results.
APPLIED INTELLIGENCE
(2021)
Article
Computer Science, Interdisciplinary Applications
Tianyu Shen, Kunkun Hao, Chao Gou, Fei-Yue Wang
Summary: This study proposes a novel method to generate various mass images using GANs and insert synthetic lesions into healthy screening mammograms, achieving data augmentation. Experimental results demonstrate the effectiveness of the method in improving detection rate.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2021)
Article
Medicine, General & Internal
Fernando Ocasio-Villa, Luisa Morales-Torres, Norma Velez-Medina, Luis A. Cubano, Juan C. Orengo, Edu B. Suarez Martinez
Summary: Breast cancer is the leading cause of sex-specific female cancer deaths in the United States. Mammogram is the Gold Standard for breast cancer screening, but breast density affects its sensitivity and specificity. This study validates the LED-based medical device PLB by comparing it with mammogram, showing high sensitivity and specificity for early detection of breast abnormalities.
FRONTIERS IN MEDICINE
(2022)
Article
Computer Science, Artificial Intelligence
Nirase Fathima Abubacker, Azreen Azman, Shyamala Doraisamy, Masrah Azrifah Azmi Murad
Summary: This study focuses on improving the computer-aided diagnosis system for breast cancer using dynamic rule refinement technique to adapt to new evidence and enhance the performance of the classifier. By dynamically refining static rules, more accurate classification of future cases is achieved.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Jun Bai, Annie Jin, Tianyu Wang, Clifford Yang, Sheida Nabavi
Summary: This study proposes an enhanced artificial intelligence system for automatic breast cancer detection. The system utilizes previous year and current year mammogram images and fuses features to predict cancer probabilities. Experimental results show that the proposed system outperforms baseline models, with the network using distance learning achieving the best performance.
Article
Biochemical Research Methods
Hansheng Li, Jianping Li, Yuxin Kang, Chunbao Wang, Feihong Liu, Wenli Hui, Qirong Bo, Lei Cui, Jun Feng, Lin Yang
Summary: Diagnostic pathology is crucial for identifying carcinomas, and accurate quantification of pathological images can provide objective clues. The Global Bank (GLB) pathway has been proposed to guide the extraction of more RoI features, significantly improving performance and increasing the accuracy of quantitative results.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2022)
Article
Mathematical & Computational Biology
Zhizezhang Gao, Yan Zhang, RuiPeng Zhang, Xia Sun, Jun Feng
Summary: Both traditional teaching and online teaching emphasize individualized education. However, the process of exploring improvements in instructional design is hindered by the challenging task of collecting data. Existing research primarily focuses on students' exam scores and overlooks their daily practice. In this study, we propose an experimental paradigm of programming performance analysis based on students' daily practice-exam records and collect a comprehensive time-series dataset, including students' individual attributes, learning behavior, and performance. We then analyze the time-series dataset using generalized estimating equations (GEE) to examine the impact of individual attributes and learning behavior on performance. This is the first application of GEE for ordinal multinomial responses in this research field, from which we conclude that gender and major do contribute to differences in programming learning. Longer answer times and shorter cost times are associated with better performance. Regardless of gender, students tend to cram for exams and perform slightly worse in daily exercises. Finally, we provide teaching mode decisions for universities based on two important individual attributes and recommend different teaching methods for students of different genders at different time points.
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
(2022)
Article
Computer Science, Information Systems
Xiaoxi Zhang, Yuan Gao, Xin Wang, Jun Feng, Yan Shi
Summary: This paper investigates the problem of transportation mode identification using GPS trajectories and geographic information, and proposes a geographic information-fused semi-supervised method. The proposed method can train an excellent transportation mode identification model with only a few labeled samples.
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
(2022)
Article
Computer Science, Artificial Intelligence
Ephrem Afele Retta, Eiad Almekhlafi, Richard Sutcliffe, Mustafa Mhamed, Haider Ali, Jun Feng
Summary: This article introduces the Amharic Speech Emotion Dataset (ASED) which consists of four dialects and five emotions. It is the first dataset for Speech Emotion Recognition (SER) in Amharic. The dataset was created by 65 native Amharic speakers who recorded 2,474 sound samples. The resulting dataset is freely available for download.
ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING
(2023)
Article
Engineering, Civil
Boting Qu, Xinyu Ren, Jun Feng, Xin Wang
Summary: Ridesplitting is a convenient and budget-friendly for-hire transportation service that arranges shared rides on the fly, with effective rider allocation being a crucial component. The DRPP method proposed in this paper utilizes a grid network and historical GPS trajectories to predict pick-up probabilities and travel times, using ILSAS and TKdS-tree to improve efficiency for matching drivers and riders, which outperformed other methods in service rate, share rate, and rider waiting time in experiments.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Biomedical
Feihong Liu, Junwei Yang, Mingyue Feng, Zhiming Cui, Xiaowei He, Luping Zhou, Jun Feng, Dinggang Shen
Summary: Phase correction is used to reconstruct real-valued diffusion MRI data by estimating the noise-free background phase. However, signal-loss and artifacts can still occur. In this paper, we propose a complex polar coordinate system (CPCS) to analyze the phase correction procedure and identify its limitations. Based on CPCS, we develop a quantitative criterion to better exploit the background phase and propose a phase calibration procedure to improve phase correction. Experimental results on synthetic and real diffusion MRI data demonstrate the effectiveness of our proposed method in reducing signal-loss and eliminating artifacts in FA maps.
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
(2023)
Article
Computer Science, Hardware & Architecture
Dekui Wang, Jun Feng, Wei Zhou, Xingxing Hao, Xiaodan Zhang
Summary: This article presents a fast FPGA connection router called FCRoute, which is based on a novel soft routing-space pruning algorithm. FCRoute classifies routing resource nodes into high-priority and low-priority ones and consists of a fast maze search and a backtracking process. By avoiding the exploration of the majority of low-priority nodes, FCRoute maintains runtime efficiency while ensuring global search ability.
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
(2023)
Article
Computer Science, Interdisciplinary Applications
Junwei Yang, Xiao-Xin Li, Feihong Liu, Dong Nie, Pietro Lio, Haikun Qi, Dinggang Shen
Summary: Recent studies have shown that multi-contrast MRI reconstruction can further accelerate MRI acquisition by exploiting correlation between contrasts. However, most of the existing methods either focus on fixed under-sampling patterns without considering inter-contrast correlation, or do not exploit complementary information between contrasts. In this study, we propose a framework that optimizes the under-sampling pattern of a target MRI contrast by utilizing the fully-sampled reference contrast. Our approach achieved superior performance compared to commonly used under-sampling patterns and state-of-the-art methods, even with up to 8-fold under-sampling factor, on both public and in-house datasets.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2023)
Article
Computer Science, Artificial Intelligence
Xia Sun, Bo Li, Richard Sutcliffe, Zhizezhang Gao, Wenying Kang, Jun Feng
Summary: Students can develop their skills by completing a series of tailored exercises, which is more effective than choosing exercises from online sources themselves. This paper presents a novel approach called Weighting-based Student Exercise Matrix Factorization (Wse-MF) that combines student learning ability and exercise difficulty. The research results demonstrate that Wse-MF outperforms other models in cognitive diagnosis and matrix factorization in terms of prediction quality and time complexity. There is also an optimal value of the latent factor K and hyperparameter c0 for each dataset. Overall, this paper contributes to the improvement of matrix factorization in educational data.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Information Systems
Songtao Ding, Hongyu Wang, Hu Lu, Michele Nappi, Shaohua Wan
Summary: In this paper, a two-path gland segmentation algorithm of colon pathological image based on local semantic guidance is proposed. The improved candidate region search algorithm is employed to generate sub-datasets sensitive to specific features. The semantic feature-guided model is used to extract local adenocarcinoma features and enhance the network's learning ability to gland morphological features.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2023)
Article
Computer Science, Hardware & Architecture
Dekui Wang, Jun Feng, Ke Liu, Wei Zhou, Xingxing Hao, Xiaodan Zhang
Summary: This article introduces a fast FPGA connection router called PRoute, which implements a novel prerouting-based parallel local routing algorithm. PRoute precomputes potential routing solutions for various connection patterns on FPGAs, and achieves runtime efficiency and global search ability through parallel local search and A-star maze expansion. Experimental results show that PRoute achieves significant speedups without degrading the quality of results.
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
(2023)
Article
Engineering, Multidisciplinary
Mustafa Mhamed, Richard Sutcliffe, Xia Sun, Jun Feng, Ephrem Afele Retta
Summary: Sentiment analysis aims to extract emotions from textual data, and various challenges have emerged due to the proliferation of social media platforms and the flow of data in the Arabic language. This paper introduces Gated Convolution Long (GCL), an architecture designed for Arabic Sentiment Analysis, which overcomes difficulties with lengthy sequence training samples and improves performance for binary and multiple classifications. The proposed method achieves better results than baselines in various Arabic datasets, and includes a Custom Regularization Function (CRF) that enhances performance and optimizes validation loss. Furthermore, the paper explores the relationship between Modern Standard Arabic and five Arabic dialects through a cross-dialect training study.
ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH
(2023)
Article
Computer Science, Artificial Intelligence
Qiang Lu, Xia Sun, Yunfei Long, Zhizezhang Gao, Jun Feng, Tao Sun
Summary: Sentiment analysis (SA) has achieved significant breakthroughs in the past decade and there is a growing interest in multimodal SA (MSA). This article provides a comprehensive overview of SA advances, introduces a novel framework for SA tasks, and discusses the workflows and recent advances of single-modal SA. It also explores the research gaps and challenges in MSA, and proposes potential directions for future improvement.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Chemistry, Multidisciplinary
Ephrem Afele Retta, Richard Sutcliffe, Jabar Mahmood, Michael Abebe Berwo, Eiad Almekhlafi, Sajjad Ahmad Khan, Shehzad Ashraf Chaudhry, Mustafa Mhamed, Jun Feng
Summary: Cross-lingual and multilingual training can be an effective strategy for training an SER classifier when resources for a language are scarce. The difficulty of SER varies for different languages, and better results can be obtained by using two or three non-target languages for training.
APPLIED SCIENCES-BASEL
(2023)
