Article
Engineering, Biomedical
Rakesh Chandra Joshi, Saumya Yadav, Vinay Kumar Pathak, Hardeep Singh Malhotra, Harsh Vardhan Singh Khokhar, Anit Parihar, Neera Kohli, D. Himanshu, Ravindra K. Garg, Madan Lal Brahma Bhatt, Raj Kumar, Naresh Pal Singh, Vijay Sardana, Radim Burget, Cesare Alippi, Carlos M. Travieso-Gonzalez, Malay Kishore Dutta
Summary: A deep learning-based system is proposed for automatic detection and classification of COVID-19 using chest X-ray images, achieving a high accuracy rate in multi-class and binary classification. Infected patient's chest X-ray images reveal distinct opacities compared to healthy lungs, enabling a rapid and accurate diagnostic tool to assist healthcare professionals in managing the pandemic effectively.
BIOCYBERNETICS AND BIOMEDICAL ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
David Sosa-Trejo, Antonio Bandera, Martin Gonzalez, Santiago Hernandez-Leon
Summary: Since the 19th century, scientists have tried to quantify species distributions using techniques such as direct counting and microscopes. Automatic image processing and classification methods are now being utilized to avoid manual procedures for classifying marine plankton. This article summarizes the techniques proposed for classifying marine plankton from the beginning of this field to the present day, focusing on automatic methods that utilize image processing.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Review
Computer Science, Information Systems
Tahira Iqbal, Arslan Shaukat, Muhammad Usman Akram, Zartasha Mustansar, Aimal Khan
Summary: Chest radiographs are the most important diagnostic tool for thoracic pathologies, with promising results being found in automating medicine through Artificial Intelligence techniques. Studies show pneumothorax is more common in men, and deep learning models have achieved good results in classification and localization of pneumothorax.
Article
Biochemical Research Methods
Mariusz Marzec, Adam Piorkowski, Arkadiusz Gertych
Summary: In this study, a new algorithm was developed to accurately segment cell nuclei in 3D images and achieved the best results in fluorescence image evaluation.
BMC BIOINFORMATICS
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Sean Carey, Sonja Kandel, Christin Farrell, John Kavanagh, TaeBong Chung, William Hamilton, Patrik Rogalla
Summary: A novel thick-slab projection technique for ultra-low dose computed tomography (CT) was compared with conventional chest x ray in 13 diagnostic categories. The study found that the thoracic tomogram was diagnostically superior to CXR for focal lung disease, but the difference was not significant for non-focal lung disease and effusions.
Review
Computer Science, Artificial Intelligence
Shuo Meng, Ruru Pan, Weidong Gao, Benchao Yan, Yangyang Peng
Summary: This paper provides a comprehensive review of recent research on automatic recognition of woven fabric structural parameters, highlighting the drawbacks of manual operations based on human eyes and experiences and the advantages of computer-vision-based automatic methods. It offers insights for researchers in the textile industry to understand and utilize automated methods effectively.
ARTIFICIAL INTELLIGENCE REVIEW
(2022)
Article
Computer Science, Information Systems
Md. Shakhawat Hossain, Galib Muhammad Shahriar, M. M. Mahbubul Syeed, Mohammad Faisal Uddin, Mahady Hasan, Md. Sakir Hossain, Rubina Bari
Summary: Traditionally, pathological analysis and diagnosis are done manually by experts using a microscope. With the development of digital technology, whole slide images (WSI) allow for computer-based analysis and diagnosis. However, tissue artifacts can affect the accuracy of analyis and diagnosis. Current methods rely on experts for artifact severity assessment, which is time-consuming and may result in loss of important data. This paper proposes a system that utilizes convolutional neural networks (CNN) for automatic artifact detection and severity evaluation, achieving high accuracy and correlation with pathologist's evaluation.
Article
Computer Science, Information Systems
Mustafa Koc, Suat Kamil Sut, Ihsan Serhatlioglu, Mehmet Baygin, Turker Tuncer
Summary: This study presents a transfer learning-based model for automatic diagnosis of prostate cancer. The deep features of prostate MRI images are extracted using pre-trained VGG networks, and feature selection is conducted using the neighborhood component analysis algorithm. Classification is performed using the cubic k nearest neighbors algorithm, resulting in an accuracy of 98.01%.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Interdisciplinary Applications
Yu An, Jiulin Guo, Qing Ye, Conrad Childs, John Walsh, Ruihai Dong
Summary: With the explosive growth in seismic data acquisition and the successful application of deep convolutional neural networks (DCNN) to various image processing tasks within multidisciplinary fields, researchers have begun to explore DCNN-based automatic seismic interpretation techniques. By open-sourcing a multi-gigabyte expert-labelled field dataset, they demonstrate that fault recognition is an image segmentation or edge detection problem and propose a novel workflow for fault recognition.
COMPUTERS & GEOSCIENCES
(2021)
Article
Computer Science, Information Systems
Qiang Li, Yu Lai, Mohammed Jajere Adamu, Lei Qu, Jie Nie, Weizhi Nie
Summary: We propose a multi-level residual feature fusion network (MLRFNet) for classifying thoracic diseases, which can capture receptive field information and enhance disease-specific features. MLRFNet consists of a feature extractor, a multi-level residual feature classifier (MRFC), and ECA attention modules for focusing on critical pathological information.
Article
Medicine, General & Internal
Agata Gielczyk, Anna Marciniak, Martyna Tarczewska, Sylwester Michal Kloska, Alicja Harmoza, Zbigniew Serafin, Marcin Wozniak
Summary: This paper presents a lightweight approach based on machine learning methods for COVID-19 diagnostics using X-ray images. By extracting features using a convolutional neural network and classifying samples with Random Forest, XGBoost, LightGBM, and CatBoost, the study achieved effective and quick patient classification. The LightGBM model demonstrated the best performance in classifying patients based on features extracted from X-ray images.
JOURNAL OF CLINICAL MEDICINE
(2022)
Article
Engineering, Electrical & Electronic
Alexey M. Romanov
Summary: This article proposes a new method of microscopic image classification for liquid crystals-based biosensors with fast response. The method is based on topological analysis and provides 95% accuracy. It reaches eightfold performance compared to CNNs on the same hardware and has only nine parameters, which can be easily tuned based on the properties of the liquid crystals suspension and the microscope. This method is a significant step towards the creation of fully automatic biosensors for industrial water quality assessment systems.
IEEE SENSORS JOURNAL
(2023)
Article
Computer Science, Information Systems
Jianpeng An, Qing Cai, Zhiyong Qu, Zhongke Gao
Summary: The study proposed a framework for multi-appearance COVID-19 screening using lung region priors, which achieved significant performance in COVID-19 screening.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Zhigang L, Liangliang Li, Hongxi Wang, Peng Wang, Jianheng Li, Lei Shu, Xiaoyan Li
Summary: This paper proposes a pseudo-colour enhancement algorithm for displaying high-bit RAW images on low-bit monitors. The algorithm improves the display effect of super 8-bit greyscale images on ordinary 8-bit monitors, enriching the amount of information and strengthening image recognition effects.
IET IMAGE PROCESSING
(2023)
Article
Mathematics
Javier Martinez-Torres, Alicia Silva Pineiro, Alvaro Alesanco, Ignacio Perez-Rey, Jose Garcia
Summary: This study introduces an automatic method for characterizing psoriasis images, including image pre-processing, feature extraction, lesion classification, and parameter obtaining. Endorsed by a professional dermatologist, this methodology is suitable for monitoring different types of lesions.
Article
Chemistry, Analytical
Wojciech Silka, Michal Wieczorek, Jakub Silka, Marcin Wozniak
Summary: Malaria is a life-threatening disease caused by parasites transmitted through mosquito bites. Early diagnosis and treatment are crucial, especially in developing countries. A novel CNN architecture with 99.68% accuracy for malaria detection is proposed, outperforming existing approaches and showing promise in resource-limited settings. The CNN was trained on a large dataset and accurately distinguished infected and uninfected samples, with high sensitivity and specificity. An analysis of model performance on different malaria subtypes and implications for infectious disease diagnosis using deep learning are discussed.
Article
Computer Science, Information Systems
Wei Wei, Qiao Ke, Dawid Polap, Marcin Wozniak
Summary: Digital security in modern systems often relies on biometric methods, and new implementations continue to emerge. This can be seen in various applications, such as signing for a courier package pick-up. However, signature verification is a complex process due to variations in size, angle, and writing conditions. Therefore, new methods are constantly needed to evaluate signatures. In this article, the authors propose the use of spline interpolation and two types of artificial neural networks to verify the identity of a person based on selected local and global features extracted from signature images. Experimental results on the SVC2004 database demonstrate an accuracy of 87.7%.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Automation & Control Systems
Dawid Polap
Summary: This paper proposes an innovative approach to automatic monitoring data analysis system. By combining heuristic algorithms and convolutional neural networks, it can effectively analyze the pixel changes in surveillance recordings and reduce the amount of data processing, thereby improving the efficiency of the system.
Editorial Material
Plant Sciences
Weipeng Jing, Zhufang Kuang, Rafal Scherer, Marcin Wozniak
FRONTIERS IN PLANT SCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Dawid Polap
Summary: In this paper, a hybrid solution for automatic image description extraction is proposed. The network architecture includes a module for object removal and background element classification, followed by multi-branch convolutional network classification. The obtained probabilities are used to find semantic values through the Skip-Gram module. The main advantage of this solution is the ability to extend the network with additional branches to increase the number of main object classes.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Hardware & Architecture
Feng Xue, Dawid Polap
Summary: This paper proposes a method for image detail restoration of online education videos based on dual discriminant networks in order to improve the image detail restoration effect and image quality of online education videos. By utilizing grayscale changes in the selected spatial domain, the detailed features of the original video art image are enhanced, and a dual discriminant generative adversarial network model is constructed to repair the enhanced image details. Experimental results show that this method has a good image enhancement effect in restoring image detail features, improving the dynamic range and grayscale contrast of the image, and has a significant effect on image detail repair.
MOBILE NETWORKS & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Qingsen Yan, Axi Niu, Chaoqun Wang, Wei Dong, Marcin Wozniak, Yanning Zhang
Summary: Deep learning-based methods have achieved remarkable results in the field of super-resolution. However, the limitation of paired training image sets has led researchers to explore self-supervised learning. However, the assumption of inaccurate downscaling kernel functions often leads to degraded results. To address this issue, this paper introduces KGSR, a kernel-guided network that trains both upscaling and downscaling networks to generate high-quality high-resolution images even without knowing the actual downscaling process.
PATTERN RECOGNITION
(2024)
Article
Multidisciplinary Sciences
G. Prabu Kanna, S. J. K. Jagadeesh Kumar, Yogesh Kumar, Ankur Changela, Marcin Wozniak, Jana Shafi, Muhammad Fazal Ijaz
Summary: This paper aims to address the challenges in cauliflower disease identification and detection in agriculture by utilizing advanced deep transfer learning techniques, ultimately benefiting farmers and consumers.
SCIENTIFIC REPORTS
(2023)
Article
Computer Science, Information Systems
Dawid Polap, Antoni Jaszcz, Natalia Wawrzyniak, Grzegorz Zaniewicz
Summary: Automatic analysis of side-scan sonar (SSS) images is challenging due to acoustic measurement parameters and the variety of objects present. Furthermore, there is a risk of potential attacks on the seabed analysis application. To address these issues, we propose a solution based on convolutional neural networks with bilinear pooling, which improves classification accuracy by merging data from two networks. The first network analyzes the original image, while the second network analyzes the image after applying the superpixel method, allowing for the consideration of different types of features. Additionally, we introduce a poisoning detection mechanism to analyze the images and network results. Real SSS images obtained in Szczecin city, Poland are used for evaluation. The importance of this scientific research lies in its contribution to accurate analysis and safe measurements.
Article
Multidisciplinary Sciences
Mohit Kumar, Priya Mukherjee, Sahil Verma, Kavita, Jana Shafi, Marcin Wozniak, Muhammad Fazal Ijaz
Summary: The Industrial Internet of Things (IIoT) faces challenges in data privacy and security. This research paper proposes a privacy preservation model in IIoT using artificial intelligent techniques. The model involves two stages: data sanitization and restoration. The sanitization process hides sensitive information and generates optimal keys using a new algorithm. The simulation results demonstrate the superiority of the proposed model over other state-of-the-art models in terms of performance metrics.
SCIENTIFIC REPORTS
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Antoni Jaszcz, Katarzyna Prokop, Dawid Polap, Gautam Srivastava, Jerry Chun-Wei Lin
Summary: In this paper, a human-AI collaboration method is proposed for analyzing newly generated images to create virtual worlds. The method is based on using generative adversarial networks (GAN) to analyze different scenes and involve user communication to assess the created environment. User information contributes to analyzing sample quality and the potential need to retrain or rebuild the GAN model. This method enhances the perception of virtual reality.
ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, ICAISC 2022, PT I
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Alan Popiel, Marcin Wozniak
Summary: This paper presents a model and an algorithm to optimize the placement of routers in a network system for the mining industry, with N chambers and N-1 or fewer connections between them. The model considers two types of routers with different signal strengths, and the algorithm has a computational complexity of O(n(2), as tested on sample graph structures.
ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, ICAISC 2022, PT II
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Jakub Silka, Michal Wieczorek, Martyna Kobielnik, Marcin Wozniak
Summary: Deep learning architectures are used for demanding analysis of complex data inputs, where regular neural networks may encounter issues. In this article, we propose a deep learning model based on a BiLSTM neural network architecture. The proposed model is trained using the Adam algorithm, and we also examine other latest algorithms to determine the best configuration. Results show that our proposed BiLSTM deep learning neural network achieves over 99% accuracy.
ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, ICAISC 2022, PT I
(2023)
Article
Computer Science, Information Systems
Jyotismita Chaki, Marcin Wozniak
Summary: This study proposes a reinforcement learning agent that can interact with brain tumor images to retrieve and categorize similar images. The proposed method utilizes a novel architecture and binary coding technique, as well as fuzzy logic-based sample generation, to improve brain tumor classification and retrieval.
Article
Computer Science, Artificial Intelligence
Marcin Wozniak, Jozef Szczotka, Andrzej Sikora, Adam Zielonka
Summary: This article presents a model of adjustable moisture control for historical buildings, utilizing a flexible IoT infrastructure and type-2 fuzzy logic reasoning to create an innovative intelligent system for interior conditions control. The developed system, tested in an old brewery building, showed efficient dehumidification results at a low cost.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Interdisciplinary Applications
Alireza Karimi, Reza Razaghi, Siddharth Daniel D'costa, Saeed Torbati, Sina Ebrahimi, Seyed Mohammadali Rahmati, Mary J. Kelley, Ted S. Acott, Haiyan Gong
Summary: This study investigated the biomechanical properties of the conventional aqueous outflow pathway using fluid-structure interaction. The results showed that the distribution of aqueous humor wall shear stress within this pathway is not uniform, which may contribute to our understanding of the underlying selective mechanisms.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Robert V. Bergen, Jean-Francois Rajotte, Fereshteh Yousefirizi, Arman Rahmim, Raymond T. Ng
Summary: This article introduces a 3D generative model called TrGAN, which can generate medical images with important features and statistical properties while protecting privacy. By evaluating through a membership inference attack, the fidelity, utility, and privacy trade-offs of the model were studied.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Hoda Mashayekhi, Mostafa Nazari, Fatemeh Jafarinejad, Nader Meskin
Summary: In this study, a novel model-free adaptive control method based on deep reinforcement learning (DRL) is proposed for cancer chemotherapy drug dosing. The method models the state variables and control action in their original infinite spaces, providing a more realistic solution. Numerical analysis shows the superior performance of the proposed method compared to the state-of-the-art RL-based approach.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Hao Sun, Bao Li, Liyuan Zhang, Yanping Zhang, Jincheng Liu, Suqin Huang, Xiaolu Xi, Youjun Liu
Summary: In cases of moderate stenosis in the internal carotid artery, the A1 segment of the anterior cerebral artery or the posterior communicating artery within the Circle of Willis may show a hemodynamic environment with high OSI and low TAWSS, increasing the risk of atherosclerosis development and stenosis in the CoW.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Ilaria Toniolo, Paola Pirini, Silvana Perretta, Emanuele Luigi Carniel, Alice Berardo
Summary: This study compared the outcomes of endoscopic sleeve gastroplasty (ESG) and laparoscopic sleeve gastrectomy (LSG) in weight loss surgery using computational models of specific patients. The results showed significant differences between the two procedures in terms of stomach volume reduction and mechanical stimulation. A predictive model was proposed to support surgical planning and estimation of volume reduction after ESG.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Chun-You Chen, Ya-Lin Chen, Jeremiah Scholl, Hsuan-Chia Yang, Yu-Chuan (Jack) Li
Summary: This study evaluated the overall performance of a machine learning-based CDSS (MedGuard) in triggering clinically relevant alerts and intercepting inappropriate drug errors and LASA drug errors. The results showed that MedGuard has the ability to improve patients' safety by triggering clinically valid alerts.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Lingzhi Tang, Xueqi Wang, Jinzhu Yang, Yonghuai Wang, Mingjun Qu, HongHe Li
Summary: In this paper, a dynamical local feature fusion net for automatically recognizing aortic valve calcification (AVC) from echocardiographic images is proposed. The network segments high-echo areas and adjusts the selection of local features to better integrate global and local semantic representations. Experimental results demonstrate the effectiveness of the proposed approach.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
You-Lei Fu, Wu Song, Wanni Xu, Jie Lin, Xuchao Nian
Summary: This study investigates the combination of surface electromyographic signals (sEMG) and deep learning-based CNN networks to study the interaction between humans and products and the impact on body comfort. It compares the advantages and disadvantages of different CNN networks and finds that DenseNet has unique advantages over other algorithms in terms of accuracy and ease of training, while mitigating issues of gradient disappearance and model degradation.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Moritz Rempe, Florian Mentzel, Kelsey L. Pomykala, Johannes Haubold, Felix Nensa, Kevin Kroeninger, Jan Egger, Jens Kleesiek
Summary: In this study, a deep learning-based skull stripping algorithm for MRI was proposed, which works directly in the complex valued k-space and preserves the phase information. The results showed that the algorithm achieved similar results to the ground truth, with higher accuracy in the slices above the eye region. This approach not only preserves valuable information for further diagnostics, but also enables immediate anonymization of patient data before being transformed into the image domain.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Ziyang Chen, Laura Cruciani, Elena Lievore, Matteo Fontana, Ottavio De Cobelli, Gennaro Musi, Giancarlo Ferrigno, Elena De Momi
Summary: In this paper, a deep learning-based approach is proposed to recover 3D information of intra-operative scenes, which can enhance the safety of robot-assisted surgery by implementing depth estimation using stereo images.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Ao Leng, Bolun Zeng, Yizhou Chen, Puxun Tu, Baoxin Tao, Xiaojun Chen
Summary: This study presents a novel training system for zygomatic implant surgery, which offers a more realistic simulation and training solution. By integrating visual, haptic, and auditory feedback, the system achieves global rigid-body collisions and soft tissue simulation, effectively improving surgeons' proficiency.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Yingjie Wang, Xueqing Yin
Summary: This study developed an integrated computational model combining coronary flow and myocardial perfusion models to achieve physiologically accurate simulations. The model has the potential for clinical application in diagnosing insufficient myocardial perfusion.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Nitzan Avidan, Moti Freiman
Summary: This study aims to enhance the generalization capabilities of DNN-based MRI reconstruction methods for undersampled k-space data. By introducing a mask-aware DNN architecture and training method, the under-sampled data and mask are encoded within the model structure, leading to improved performance. Rigorous testing on the widely accessible fastMRI dataset reveals that this approach demonstrates better generalization capabilities and robustness compared to traditional DNN methods.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Enhao Zhang, Saeed Miramini, Lihai Zhang
Summary: This study investigates the combined effects of osteoporosis and diabetes on fracture healing process by developing numerical models. The results show that osteoporotic fractures have higher instability and disruption in mesenchymal stem cells' proliferation and differentiation compared to non-osteoporotic fractures. Moreover, when osteoporosis coexists with diabetes, the healing process of fractures can be severely impaired.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)
Article
Computer Science, Interdisciplinary Applications
Yunhao Bai, Wenqi Li, Jianpeng An, Lili Xia, Huazhen Chen, Gang Zhao, Zhongke Gao
Summary: This study proposes an effective MIL method for classifying WSI of esophageal cancer. The use of self-supervised learning for feature extractor pretraining enhances feature extraction from esophageal WSI, leading to more robust and accurate performance. The proposed framework outperforms existing methods, achieving an accuracy of 93.07% and AUC of 95.31% on a comprehensive dataset of esophageal slide images.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2024)