Article
Computer Science, Information Systems
Osama S. Faragallah, Heba El-Hoseny, Walid El-Shafai, Wael Abd El-Rahman, Hala S. El-sayed, El-Sayed El-Rabaie, Fathi Abd El-Samie, Korany R. Mahmoud, Gamal G. N. Geweid
Summary: This article proposes a complete fusion system for medical images based on multi-resolution, multi-scale transforms, and an optimized algorithm. The system achieves optimal matching between input images and minimal artifacts through four stages, and enhances image clarity and visualization using adaptive histogram equalization and histogram matching. Experimental results demonstrate that the proposed fusion algorithms perform well in terms of image quality and can aid in disease diagnosis.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Software Engineering
Roberto M. Dyke, Kai Hormann
Summary: This paper presents a novel technique that improves the naive approach of histogram equalization by linearly interpolating the cumulative distribution of a low-bit image. The proposed method is capable of producing a high entropy equalized histogram while preserving distances between similar intensities.
Article
Multidisciplinary Sciences
Enes Ayan, Bergen Karabulut, Halil Murat Unver
Summary: The study aimed to develop a computer-aided pneumonia detection system using a convolutional neural network ensemble method for automatic diagnosis of pneumonia in children. The proposed ensemble method achieved remarkable results with an AUC of 95.21 and a sensitivity of 97.76 on the test data, while also achieving a classification accuracy of 90.71 in distinguishing normal, viral pneumonia, and bacterial pneumonia in chest X-ray images.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Junding Sun, Xiang Li, Chaosheng Tang, Shui-Hua Wang, Yu-Dong Zhang
Summary: An improved intelligent global optimization algorithm was proposed by the research team to optimize the hyperparameters of models for better detection of COVID-19 and pneumonia. Experimental results showed that the momentum factor biogeography-based optimization outperformed biogeography-based optimization in optimizing convolutional neural networks, enhancing detection performance.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Biology
Khabir Uddin Ahamed, Manowarul Islam, Ashraf Uddin, Arnisha Akhter, Bikash Kumar Paul, Mohammad Abu Yousuf, Shahadat Uddin, Julian M. W. Quinn, Mohammad Ali Moni
Summary: COVID-19 is a severe respiratory viral disease, and a deep learning-based case detection model was developed in this study, trained with chest CT scans and X-ray images, achieving high accuracy in diagnosing COVID-19 cases.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Mathematics, Interdisciplinary Applications
Emtiaz Hussain, Mahmudul Hasan, Md Anisur Rahman, Ickjai Lee, Tasmi Tamanna, Mohammad Zavid Parvez
Summary: A novel CNN model called CoroDet was proposed for automatic detection of COVID-19 using raw chest X-ray and CT scan images in this study. The model outperformed existing techniques in terms of classification accuracy, providing a solution to the issue of scarcity of COVID-19 testing kits.
CHAOS SOLITONS & FRACTALS
(2021)
Article
Optics
Ahmed S. Elkorany, Zeinab F. Elsharkawy
Summary: The study proposes a medical system called COVIDetectionNet based on Deep Learning for automated detection of COVID-19 infection from chest radiography images. The system achieved high accuracy in detecting and classifying COVID-19 infections, outperforming other methods in various evaluation metrics.
Article
Computer Science, Information Systems
J. Arun Prakash, C. R. Asswin, Vinayakumar Ravi, V Sowmya, K. P. Soman
Summary: Pediatric pneumonia has high mortality rates and is challenging to diagnose. This study proposes a stacking classifier model based on deep learning-based feature fusion and image enhancement for pediatric pneumonia diagnosis. The model achieves high accuracy and recall rates on a publicly available dataset, and is expected to be valuable for real-time diagnosis.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Chemistry, Analytical
Muhammad Tahir Naseem, Tajmal Hussain, Chan-Su Lee, Muhammad Adnan Khan
Summary: This study proposes a method for automatic detection of COVID-19 and other chest-related diseases using digital chest X-ray images. By applying transfer learning algorithms and data augmentation for training, the model achieved high accuracy in multiple classification tasks.
Article
Computer Science, Information Systems
Roaa Alsharif, Yazan Al-Issa, Ali Mohammad Alqudah, Isam Abu Qasmieh, Wan Azani Mustafa, Hiam Alquran
Summary: Pneumonia is an inflammatory disease of the lung parenchyma caused by various infectious microorganisms and non-infective agents. Radiological images, such as Chest X-ray, provide early detection and prompt action. Early and accurate detection is crucial to prevent fatal consequences, especially in children and seniors.
Article
Computer Science, Artificial Intelligence
Ahmed Salem Musallam, Ahmed Sobhy Sherif, Mohamed K. Hussein
Summary: This study introduces an effective Deep Convolutional Neural Network (DCNN) called DeepChest for fast and accurate detection of both COVID-19 and Pneumonia in chest X-ray images. The proposed approach achieves high accuracy in detecting COVID-19 and Pneumonia.
EGYPTIAN INFORMATICS JOURNAL
(2022)
Article
Computer Science, Information Systems
P. Pravin Sironmani, M. Gethsiyal Augasta
Summary: This research introduces a new technique called Channelization to improve the efficiency of CNN in classifying histopathological medical images by extracting refined features.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Construction & Building Technology
Jian Liu, Zhiyuan Zhao, Chengshun Lv, Yunfeng Ding, Honglei Chang, Quanyi Xie
Summary: This paper proposes an image enhancement algorithm applied to road tunnel crack data and examines its effectiveness using three different object detection models. The experiments show that a transfer detection model can be established between road tunnel crack data and road crack data, and the detection result of YOLOv5 model is the best among the three network models.
CONSTRUCTION AND BUILDING MATERIALS
(2022)
Article
Chemistry, Multidisciplinary
Yinggang Xie, Quan Wang, Yuanxiong Chang, Xueyuan Zhang
Summary: This study proposes a novel fast target recognition algorithm for dynamic scene moving target recognition. By combining adaptive histogram equalization with the ORB algorithm, the algorithm improves the matching effect under illumination challenges. Experimental results demonstrate its superiority in matching, robustness, and real-time performance.
APPLIED SCIENCES-BASEL
(2022)
Article
Biology
Mehreen Sirshar, Taimur Hassan, Muhammad Usman Akram, Shoab Ahmed Khan
Summary: The study explores the use of deep learning systems to diagnose pulmonary diseases, but the challenges of large-scale data and rare diseases limit the application of such systems.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Chemistry, Multidisciplinary
Abdolhamid Esmaeeli, Zahra Keshavarz, Firoozeh Dehdar, Majid Assadi, Mohammad Seyedabadi
Summary: In this study, the effects of carvedilol, metoprolol and propranolol on cisplatin-induced nephrotoxicity in an animal model were investigated. The researchers found that carvedilol and high-dose propranolol may offer potential therapeutic benefits in cisplatin-induced nephrotoxicity.
DRUG AND CHEMICAL TOXICOLOGY
(2022)
Article
Computer Science, Information Systems
Hossein Salemi, Habib Rostami, Saeed Talatian-Azad, Mohammad Reza Khosravi
Summary: This paper proposes a method for predicting DDoS attacks based on Lyapunov Exponent Analysis and Echo State Network (LEAESN). By predicting the future traffic of the network and calculating the time series of prediction errors, attacks can be detected and predicted. Testing on the Darpa98 dataset demonstrates that LEAESN has the capability to predict DDoS attacks.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Pharmacology & Pharmacy
Ramin Seyedian, Elham Shabankareh Fard, Seyede Sahar Hashemi, Hossein Hasanzadeh, Majid Assadi, Sasan Zaeri
Summary: The study found that nanofibers loaded with 4% diltiazem have a significant promoting effect on wound healing, increasing fibroblast proliferation and demonstrating excellent healing effects in animal wound healing experiments.
PHARMACEUTICAL DEVELOPMENT AND TECHNOLOGY
(2021)
Review
Chemistry, Multidisciplinary
Azin Moradbeikie, Ahmad Keshavarz, Habib Rostami, Sara Paiva, Sergio Ivan Lopes
Summary: This article discusses the adoption of IoT in various application domains for performance improvement and cost reduction. It highlights the importance of LPWAN technologies for outdoor localization and presents different proposed methods for obtaining IoT relative location. The paper identifies limitations in current localization methods and provides insights into research works published between 2018 to July 2021 in the Google Scholar database.
APPLIED SCIENCES-BASEL
(2021)
Article
Geochemistry & Geophysics
Mohammad Shokri-Kaveh, Gholam Javan-Doloei, Reza Mansouri, Nasim Karamzadeh, Ahmad Keshavarz
Summary: In this study, an automatic S-wave onset time picking algorithm using undecimated discrete wavelet transform and autoregressive model is developed. The proposed method has been tested on synthetic seismograms and real earthquake data sets, showing promising results and the potential to replace manual phase picking.
GEOPHYSICAL JOURNAL INTERNATIONAL
(2023)
Article
Computer Science, Information Systems
Abouzar Dehdar, Ahmad Keshavarz, Naser Parhizgar
Summary: This paper proposes a steganalysis algorithm based on Modified Graph Clustering Based Ant Colony Optimization (MGCACO) feature selection and Random Forest classifier. The algorithm extracts different features related to steganalysis from each image, selects an optimal set of features using the MGCACO feature selection algorithm, and uses a trained classifier to differentiate between clean and steganography images. Additionally, the types of steganography algorithms can be identified using the proposed algorithm. Experimental results show that the algorithm can effectively distinguish different embedding rates of steganography algorithms and accurately detect the type of steganography algorithm with an average accuracy of 90%.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Review
Computer Science, Artificial Intelligence
Samira Sajed, Amir Sanati, Jorge Esparteiro Garcia, Habib Rostami, Ahmad Keshavarz, Andreia Teixeira
Summary: This paper emphasizes the importance of deep learning architectures in lung disease diagnosis using CXR images. The results show that pre-trained networks including ResNet, VGG, and DenseNet are the most frequently used CNN architectures and can significantly improve sensitivity and accuracy. The study also discusses the limitations of existing literature and potential research opportunities in using deep learning architectures for possible findings in CXR images.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Information Systems
Azin Moradbeikie, Ahmad Keshavarz, Habib Rostami, Sara Paiva, Sergio Ivan Lopes
Summary: This article addresses the challenge of low accuracy in RSSI-based outdoor IoT node localization by computing the Path Loss (PL) parameters of LoRaWAN and proposing a dual-slope PL model. The effectiveness of the model is evaluated using a publicly available LoRaWAN dataset and compared to the state-of-the-art and Cramer-Rao Lower Bound (CRLB). The median error and mean error of localization are 117m and 236m, respectively.
INTERNET OF THINGS
(2023)
Article
Quantum Science & Technology
Ebrahim Ghasemian, Abolhassan Razminia, Habib Rostami
Summary: This paper proposes a realistic model for implementing neural networks on photonic quantum computers. A quantum circuit is designed using the continuous-variable architecture and encodes information in the spectral amplitude functions of single photons. The model is able to reproduce classical neural network models while maintaining quantum phenomena.
QUANTUM INFORMATION PROCESSING
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Hamed Behzadi-Khormouji, Habib Rostami
Summary: The study introduces a method called FMO to address drawbacks of back-propagation-based and perturbation-based visualization methods, which can highlight important input features independently of the architecture; FMO also introduces a multi-resolution occlusion strategy to efficiently address the time-consumption issue of traditional occlusion test methods.
MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2021, PT I
(2021)
Article
Computer Science, Artificial Intelligence
Hamed Behzadi-Khormouji, Habib Rostami
Summary: This research introduces a perturbation-based visualization method called Fast Multi-resolution Occlusion (FMO) which is efficient in terms of time and resource consumption and can be considered in real-world applications. The method is compared with five other perturbation-based visualization methods in terms of time-consumption, visualization quality, and localization accuracy. Experimental results show that FMO is faster than other methods and outperforms them in terms of localization accuracy.
APPLIED INTELLIGENCE
(2021)
Meeting Abstract
Radiology, Nuclear Medicine & Medical Imaging
M. Assadi, E. Jafari, H. Ahmadzadehfar, D. Bagheri, A. Amini
EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING
(2020)
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)