Article
Chemistry, Analytical
Buket D. Barkana, Ahmed El-Sayed, Rana H. Khaled, Maha Helal, Hussein Khaled, Ruba Deeb, Mark Pitcher, Ruth Pfeiffer, Marilyn Roubidoux, Catherine Schairer, Amr S. Soliman
Summary: Inflammatory breast cancer (IBC) is an aggressive type of breast cancer with shorter survival than other types. Clinical presentation in North Africa does not align with the diagnosis criteria. Limited expertise exists due to the rarity of the disease. A computer-aided diagnosis system using bilateral mammograms was proposed, achieving high accuracy.
Article
Biotechnology & Applied Microbiology
Yifeng Dou, Wentao Meng
Summary: This paper introduces the research, prediction, and diagnosis methods of breast cancer, using the improved optimization algorithm GSP_SVM, which shows excellent performance in breast cancer diagnosis and improves the diagnostic efficiency of medical institutions.
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
(2021)
Review
Computer Science, Artificial Intelligence
Ganeshsree Selvachandran, Shio Gai Quek, Raveendran Paramesran, Weiping Ding, Le Hoang Son
Summary: The exponential increase in the number of diabetics has led to a rise in diabetic retinopathy cases. Developing automated DR detection methods is crucial to reduce the burden on ophthalmologists. This review summarizes the recent developments in automated DR detection using fundus images, with a focus on machine learning algorithms.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Information Systems
Tanner McWhorter, Marcin Morys, Stacie Severyn, Sean Stevens, Louis Chan, Chi-Hao Cheng
Summary: This article presents a machine learning aided electronic warfare (EW) system that uses an automatic decision tree generator, a fuzzy logic model, and a LSTM neural network to engage multiple multifunction radars effectively.
Article
Green & Sustainable Science & Technology
Hosam Alhakami, Tahani Alsubait, Wajdi Alhakami, Hatim Alhakami, Rushdi Alhakami, Mohammed Alhakami, Raees Ahmad Khan, Md Tarique Jamal Ansari
Summary: This research emphasizes the importance of effective communication with older patients in nursing, and presents the most efficient methods for improving nurse-elderly-patient communication through the study of various therapeutic communication approaches. The findings of this study have significant implications for elderly patient care in Saudi Arabia, and help establish trust and increase communication.
Article
Medicine, General & Internal
Ozum Tuncyurek, Koray Kadam, Berna Uzun, Dilber Uzun Ozsahin
Summary: This study examined the validity of the American College of Radiology appropriateness criteria for radiological imaging in diagnosing acute appendicitis and evaluated the choice of radiological examinations using fuzzy-based models. The results suggest that non-contrast computed tomography (CT) should be the first choice for patients with low and high Appendicitis Inflammatory Response (AIR) scores, while ultrasound (US) examination ranked third in high score patients.
Article
Biotechnology & Applied Microbiology
Ahmed M. Zaalouk, Gamal A. Ebrahim, Hoda K. Mohamed, Hoda Mamdouh Hassan, Mohamed M. A. Zaalouk
Summary: In this paper, a computer-aided diagnosis system based on deep learning is developed to assist pathologists in diagnosing breast cancer. Multiple pre-trained convolutional neural network models are analyzed and tested, and a new approach for transfer learning is introduced. The experimental results show that the Xception model performs the best among the tested models.
BIOENGINEERING-BASEL
(2022)
Article
Computer Science, Artificial Intelligence
Gokce Caylak Kayaturan, Gokhan Ozcelik, Aytuel Gokce
Summary: This study proposes a research methodology based on fuzzy Multi-Criteria Decision Making (MCDM) techniques to offer alternative routes during data transmission on any multi-attribute computer network under uncertainty for the first time. By employing triangular fuzzy numbers, the values of the possible risks and the probability of false positives (FP) of the transmission edges are defined. The most appropriate and reliable transmission pathways are determined, and managerial insights and main findings are provided for the network users.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Review
Oncology
Xin Yu Liew, Nazia Hameed, Jeremie Clos
Summary: Early detection and timely treatment of breast cancer can reduce the risk of death, with histopathology images and CAD systems being key technologies. Machine learning methods are increasingly applied in diagnosing breast cancer, helping to improve accuracy.
Article
Health Care Sciences & Services
Jeewoo Yoon, Jinyoung Han, Junseo Ko, Seong Choi, Ji In Park, Joon Seo Hwang, Jeong Mo Han, Daniel Duck-Jin Hwang
Summary: This study proposes an artificial intelligence-based computer-aided diagnosis system for diagnosing retinal diseases using optical coherence tomography (OCT) images in ophthalmology. Through observer performance tests and evaluation of the deep learning algorithm, the system outperforms ophthalmologists in diagnosing central serous chorioretinopathy (CSC). Especially with the assistance of AI, nonretina specialists can achieve expert-level diagnostic performance.
JOURNAL OF MEDICAL INTERNET RESEARCH
(2023)
Article
Automation & Control Systems
Soumyadeep Samonto, Samarjit Kar, Sagarika Pal, Ozkan Atan, Arif Ahmed Sekh
Summary: This paper discusses an intelligent relaying scheme based on expert systems and fuzzy algorithms to control multiple loads connected to a single feeder line. Through testing and validation, the fastest coordination time was successfully achieved under the IEEE 13 Bus system.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Wilfrido Gomez-Flores, Maria Julia Gregorio-Calas, Wagner Coelho de Albuquerque Pereira
Summary: The development of breast ultrasound CAD systems requires a set of annotated images, and this publicly available BUS dataset greatly increases the number of annotated cases and includes standardized partitions for objective evaluation and comparison of CAD systems.
Article
Computer Science, Artificial Intelligence
Shi Qiu, Yi Jin, Songhe Feng, Tao Zhou, Yidong Li
Summary: Dwarfism refers to the condition where children of the same gender and age are lower than two standard deviations of normal height in the same environment. A computer-aided diagnosis model based on brain image data and clinical features is established for the first time, along with a dwarfism prediction algorithm using multimodal pyradiomics.
INFORMATION FUSION
(2022)
Article
Computer Science, Artificial Intelligence
Xiaofeng Qi, Fasheng Yi, Lei Zhang, Yao Chen, Yong Pi, Yuanyuan Chen, Jixiang Guo, Jianyong Wang, Quan Guo, Jilan Li, Yi Chen, Qing Lv, Zhang Yi
Summary: Ultrasonography of the breast mass is an important imaging technology for diagnosing breast cancer, and ultrasound equipment is widely used in medical institutions in China. This study develops an automated breast cancer diagnosis system deployed on mobile phones, which improves diagnostic accuracy and aids in the early screening and diagnosis of breast cancer, reducing morbidity and mortality.
Article
Computer Science, Artificial Intelligence
Enrique Brazalez, Hermenegilda Macia, Gregorio Diaz, Maria-Teresa Baeza-Romero, Edelmira Valero, Valentin Valero
Summary: This study presents a decision support system called FUME, which processes real-time data to provide recommendations for reducing urban air pollution. The system combines fuzzy logic and complex event processing technology to improve the decision-making process by considering environmental conditions and providing action recommendations for different sources of pollution.
APPLIED SOFT COMPUTING
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Romulo Bourget Novas, Valeria Paula Sassoli Fazan, Joaquim Cezar Felipe
JOURNAL OF DIGITAL IMAGING
(2016)
Article
Computer Science, Interdisciplinary Applications
Alessandra A. Macedo, Hugo C. Pessotti, Luciana F. Almansa, Joaquim C. Felipe, Edna T. Kimura
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2016)
Article
Engineering, Biomedical
Antonio Carlos da Silva Senra Filho, Juliano Jinzenji Duque, Luiz Eduardo Virgilio Silva, Joaquim Cesar Felipe, Valeria Paula Sassoli Fazan, Luiz Otavio Murta Junior
JOURNAL OF MEDICAL AND BIOLOGICAL ENGINEERING
(2019)
Article
Multidisciplinary Sciences
Jeaneth Machicao, Edilson A. Correa, Gisele H. B. Miranda, Diego R. Amancio, Odemir M. Bruno
Article
Engineering, Electrical & Electronic
Luiz E. V. Silva, Juliano J. Duque, Joaquim C. Felipe, Luiz O. Murta, Anne Humeau-Heurtier
Article
Engineering, Electrical & Electronic
Gisele H. B. Miranda, Jeaneth Machicao, Odemir M. Bruno
DIGITAL SIGNAL PROCESSING
(2018)
Article
Multidisciplinary Sciences
Gisele Helena Barboni Miranda, Jeaneth Machicao, Odemir Martinez Bruno
SCIENTIFIC REPORTS
(2016)
Article
Infectious Diseases
Gisele H. B. Miranda, Jan M. Baetens, Nathalie Bossuyt, Odemir M. Bruno, Bernard De Baets
Article
Environmental Sciences
Sina Borzooei, Gisele H. B. Miranda, Ramesh Teegavarapu, Gerardo Scibilia, Lorenza Meucci, Maria Chiara Zanetti
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2019)
Article
Radiology, Nuclear Medicine & Medical Imaging
Vanessa De Araujo Faria, Mehran Azimbagirad, Gustavo Viani Arruda, Juliana Fernandes Pavoni, Joaquim Cezar Felipe, Elza Maria Carneiro Mendes Ferreira dos Santos, Luiz Otavio Murta Junior
Summary: This study introduces a reliable method using artificial intelligence neural network and PyRadiomics features to predict and detect radiation-related caries (RRC) in head and neck cancer patients under radiotherapy, achieving a sensitivity of 98.8% for RRC detection and an accuracy of 99.2% for RRC prediction.
JOURNAL OF DIGITAL IMAGING
(2021)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Matheus de Freitas Oliveira Baffa, Luciano Bachmann, Thiago Martini Pereira, Joaquim Cezar Felipe
Summary: This study aims to investigate the possibility of characterizing cancerous, normal, and inflammatory thyroid tissue by analyzing its radiation absorbance level over the hyperspectral point of view. Despite being a complex task, hyperspectral signals have shown themselves to be a promising tool for characterizing different tissues over the infrared spectrum.
2021 IEEE 34TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Samuel Zanferdini Oliva, Joaquim Cezar Felipe
INFORMATION TECHNOLOGY AND SYSTEMS, ICITS 2020
(2020)
Proceedings Paper
Computer Science, Artificial Intelligence
Rogerio Adriano de Sousa, Ana Carolina Mieko Omoto, Rubens Fazan Junior, Joaquim Cezar Felipe
INFORMATION TECHNOLOGY AND SYSTEMS, ICITS 2020
(2020)
Article
Biology
Yuri Ferretti, Newton Shydeo Brandao Miyoshi, Wilson Araujo Silva, Joaquim Cezar Felipe
COMPUTERS IN BIOLOGY AND MEDICINE
(2017)
Article
Radiology, Nuclear Medicine & Medical Imaging
L. E. V. Silva, A. C. S. Senra Filho, V. P. S. Fazan, J. C. Felipe, L. O. Murta Junior
BIOMEDICAL PHYSICS & ENGINEERING EXPRESS
(2016)
Article
Biology
Seyyed Bahram Borgheai, Alyssa Hillary Zisk, John McLinden, James Mcintyre, Reza Sadjadi, Yalda Shahriari
Summary: This study proposed a novel personalized scheme using fNIRS and EEG as the main tools to predict and compensate for the variability in BCI systems, especially for individuals with severe motor deficits. By establishing predictive models, it was found that there were significant associations between the predicted performances and the actual performances.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Hongliang Guo, Hanbo Liu, Ahong Zhu, Mingyang Li, Helong Yu, Yun Zhu, Xiaoxiao Chen, Yujia Xu, Lianxing Gao, Qiongying Zhang, Yangping Shentu
Summary: In this paper, a BDSMA-based image segmentation method is proposed, which improves the limitations of the original algorithm by combining SMA with DE and introducing a cooperative mixing model. The experimental results demonstrate the superiority of this method in terms of convergence speed and precision compared to other methods, and its successful application to brain tumor medical images.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jingfei Hu, Linwei Qiu, Hua Wang, Jicong Zhang
Summary: This study proposes a novel semi-supervised point consistency network (SPC-Net) for retinal artery/vein (A/V) classification, addressing the challenges of specific tubular structures and limited well-labeled data in CNN-based approaches. The SPC-Net combines an AVC module and an MPC module, and introduces point set representations and consistency regularization to improve the accuracy of A/V classification.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Omair Ali, Muhammad Saif-ur-Rehman, Tobias Glasmachers, Ioannis Iossifidis, Christian Klaes
Summary: This study introduces a novel hybrid model called ConTraNet, which combines the strengths of CNN and Transformer neural networks, and achieves significant improvement in classification performance with limited training data.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Juan Antonio Valera-Calero, Dario Lopez-Zanoni, Sandra Sanchez-Jorge, Cesar Fernandez-de-las-Penas, Marcos Jose Navarro-Santana, Sofia Olivia Calvo-Moreno, Gustavo Plaza-Manzano
Summary: This study developed an easy-to-use application for assessing the diagnostic accuracy of digital pain drawings (PDs) compared to the classic paper-and-pencil method. The results demonstrated that digital PDs have higher reliability and accuracy compared to paper-and-pencil PDs, and there were no significant differences in assessing pain extent between the two methods. The PAIN EXTENT app showed good convergent validity.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Biao Qu, Jialue Zhang, Taishan Kang, Jianzhong Lin, Meijin Lin, Huajun She, Qingxia Wu, Meiyun Wang, Gaofeng Zheng
Summary: This study proposes a deep unrolled neural network, pFISTA-DR, for radial MRI image reconstruction, which successfully preserves image details using a preprocessing module, learnable convolution filters, and adaptive threshold.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Alireza Rafiei, Milad Ghiasi Rad, Andrea Sikora, Rishikesan Kamaleswaran
Summary: This study aimed to improve machine learning model prediction of fluid overload by integrating synthetic data, which could be translated to other clinical outcomes.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jinlian Ma, Dexing Kong, Fa Wu, Lingyun Bao, Jing Yuan, Yusheng Liu
Summary: In this study, a new method based on MDenseNet is proposed for automatic segmentation of nodular lesions from ultrasound images. Experimental results demonstrate that the proposed method can accurately extract multiple nodules from thyroid and breast ultrasound images, with good accuracy and reproducibility, and it shows great potential in other clinical segmentation tasks.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jiabao Sheng, SaiKit Lam, Jiang Zhang, Yuanpeng Zhang, Jing Cai
Summary: Omics fusion is an important preprocessing approach in medical image processing that assists in various studies. This study aims to develop a fusion methodology for predicting distant metastasis in nasopharyngeal carcinoma by mitigating the disparities in omics data and utilizing a label-softening technique and a multi-kernel-based neural network.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Zhenxiang Xiao, Liang He, Boyu Zhao, Mingxin Jiang, Wei Mao, Yuzhong Chen, Tuo Zhang, Xintao Hu, Tianming Liu, Xi Jiang
Summary: This study systematically investigates the functional connectivity characteristics between gyri and sulci in the human brain under naturalistic stimulus, and identifies unique features in these connections. This research provides novel insights into the functional brain mechanism under naturalistic stimulus and lays a solid foundation for accurately mapping the brain anatomy-function relationship.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Qianqian Wang, Mingyu Zhang, Aohan Li, Xiaojun Yao, Yingqing Chen
Summary: The development of PARP-1 inhibitors is crucial for the treatment of various cancers. This study investigates the structural regulation of PARP-1 by different allosteric inhibitors, revealing the basis of allosteric inhibition and providing guidance for the discovery of more innovative PARP-1 inhibitors.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Qing Xu, Wenting Duan
Summary: In this paper, a dual attention supervised module, named DualAttNet, is proposed for multi-label lesion detection in chest radiographs. By efficiently fusing global and local lesion classification information, the module is able to recognize targets with different sizes. Experimental results show that DualAttNet outperforms baselines in terms of mAP and AP50 with different detection architectures.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Kaja Gutowska, Piotr Formanowicz
Summary: The primary aim of this research is to propose algorithms for identifying significant reactions and subprocesses within biological system models constructed using classical Petri nets. These solutions enable two analysis methods: importance analysis for identifying critical individual reactions to the model's functionality and occurrence analysis for finding essential subprocesses. The utility of these methods has been demonstrated through analyses of an example model related to the DNA damage response mechanism. It should be noted that these proposed analyses can be applied to any biological phenomenon represented using the Petri net formalism. The presented analysis methods extend classical Petri net-based analyses, enhancing our comprehension of the investigated biological phenomena and aiding in the identification of potential molecular targets for drugs.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Hansle Gwon, Imjin Ahn, Yunha Kim, Hee Jun Kang, Hyeram Seo, Heejung Choi, Ha Na Cho, Minkyoung Kim, Jiye Han, Gaeun Kee, Seohyun Park, Kye Hwa Lee, Tae Joon Jun, Young-Hak Kim
Summary: Electronic medical records have potential in advancing healthcare technologies, but privacy issues hinder their full utilization. Deep learning-based generative models can mitigate this problem by creating synthetic data similar to real patient data. However, the risk of data leakage due to malicious attacks poses a challenge to traditional generative models. To address this, we propose a method that employs local differential privacy (LDP) to protect the model from attacks and preserve the privacy of training data, while generating medical data with reasonable performance.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Siwei Tao, Zonghan Tian, Ling Bai, Yueshu Xu, Cuifang Kuang, Xu Liu
Summary: This study proposes a transfer learning-based method to address the phase retrieval problem in grating-based X-ray phase contrast imaging. By generating a training dataset and using deep learning techniques, this method improves image quality and can be applied to X-ray 2D and 3D imaging.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)