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Enhanced Pneumonia Detection in Chest X-Rays Using Hybrid Convolutional and Vision Transformer Networks
Authors: Benzorgat Mustapha, Yatong Zhou, Chunyan Shan and Zhitao XiaoAvailable online: 09 January 2025More LessObjective: The objective of this research is to enhance pneumonia detection in chest X-rays by leveraging a novel hybrid deep learning model that combines Convolutional Neural Networks (CNNs) with modified Swin Transformer blocks. This study aims to significantly improve diagnostic accuracy, reduce misclassifications, and provide a robust, deployable solution for underdeveloped regions where access to conventional Read More
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The Value of Using Quantitative MRI based on Synthetic Acquisition and Apparent Diffusion Coefficient to Monitor Multiple Sclerosis Lesion Activity
Authors: Abdullah H. Abujamea, Fahad B. Albadr and Arwa M. AsiriAvailable online: 09 January 2025More LessBackground: Multiple sclerosis (MS) is one of the most common disabling central nervous system diseases affecting young adults. Magnetic resonance imaging (MRI) is an essential tool for diagnosing and following up multiple sclerosis. Over the years, many MRI techniques have been developed to improve the sensitivity of MS disease detection. In recent years synthetic MRI (sMRI) and quantitative MRI (qMRI) have gain Read More
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Lightweight Lung-nodule Detection Model Combined with Multidimensional Attention Convolution
Authors: He-He Huang, Yuetao Zhao, Sen-Yu Wei, Chen Zhao, Yu Shi, Yuan Li, Weijia Huang, Yifei Yang and Jianhua XuAvailable online: 02 January 2025More LessBackground: Early and timely detection of pulmonary nodules and initiation treatment can substantially improve the survival rate of lung carcinoma. However, current detection methods based on convolutional neural networks (CNNs) cannot easily detect pulmonary nodules owing to low detection accuracy and the difficulty in detecting small-sized pulmonary nodules; meanwhile, more accurate CNN-based models are Read More
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A Comparative Study on CT-guided Radiofrequency Ablation and Targeted Therapy: Intervention Efficacy and Survival Rates in Lung Cancer Patients
Authors: Tianyu Zhao, Chunjing Zhang, Hang Dai, Jingyu Li, Liguo Hao and Yanan LiuAvailable online: 02 January 2025More LessObjective: The study aimed to evaluate the clinical efficacy of CT-guided radiofrequency ablation in conjunction with targeted therapy in lung cancer patients. Methods: We retrospectively analyzed 80 lung cancer patients. They were stratified into the Observation Group (OG; n=40, treated with CT-guided radiofrequency ablation in conjunction with targeted therapy) and the Control Group (CG; n=40, treated solely with targe Read More
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Intracranial Structural Malformations in Children in Tibet: CT and MRI Findings in a Single Tertiary Center
Authors: Xuan Yin, Dawa Ciren, Ciren Guojie, Guofu Zhang, Jimei Wang and He ZhangAvailable online: 02 January 2025More LessObjectives: The objective of this study was to summarize the findings of children’s intracranial congenital or developmental malformations found during imaging procedures in the Tibetan plateau. Methods: We retrospectively reviewed the imaging data of the suspected patients who presented with the central nervous system (CNS) malformations and were enrolled either through the clinic or after ultrasound examinations betw Read More
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Image Findings from Dual-phase Computed Tomography Pulmonary Angiography for Diagnosing Tuberculosis-associated Fibrosing Mediastinitis
Authors: Mengdi Zhang, Chao Bu, Kaiyu Jiang, Xiaozhou Long, Zhonghua Sun, Yunshan Cao and Yu LiAvailable online: 02 January 2025More LessObjective: Fibrosing mediastinitis (FM) is a rare and benign disease affecting the mediastinum and often causes pulmonary hypertension (PH). Timely diagnosis of PH caused by FM is clinically important to mitigate complications such as right heart failure in affected individuals. This retrospective study aimed to analyze the CT imaging characteristics of TB-related FM in patients with tuberculosis (TB). Additionally, the Read More
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FedPneu: Federated Learning for Pneumonia Detection across Multiclient Cross-Silo Healthcare Datasets
Authors: Shagun Sharma, Kalpna Guleria and Ayush DograAvailable online: 02 January 2025More LessBackground: Pneumonia is an acute respiratory infection that has emerged as the predominant catalyst for escalating mortality rates worldwide. In the pursuit of the prevention and prediction of pneumonia, this work employs the development of an advanced deep-learning model by using a federated learning framework. The deep learning models rely on the utilization of a centralized system for disease prediction on the me Read More
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Advanced CNN Architecture for Brain Tumor Segmentation and Classification using BraTS-GOAT 2024 Dataset
Authors: Vaidehi Satushe, Vibha Vyas, Shilpa Metkar and Davinder Paul SinghAvailable online: 02 January 2025More LessBackground: The BraTS Generalizability Across Tumors (BraTS-GoAT) initiative addresses the critical need for robust and generalizable models in brain tumor segmentation. Despite advancements in automated segmentation techniques, the variability in tumor characteristics and imaging modalities across clinical settings presents a significant challenge. Objective: This study aims to develop an advanced CNN-based model Read More
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Displaced Femoral Neck Fractures Treated with Percutaneous Compression Plates in Elderly Individuals: An Effect Analysis Based on Imaging
Authors: Huli Liu, Kai Zhao, Ying Yang, Liansheng Dai, Sanjun Gu, Haifeng Li and Yu LiuAvailable online: 02 January 2025More LessBackground: The effects of percutaneous compression plate (PCP) internal fixation for femoral neck fractures (FNFs) in elderly individuals have rarely been reported. Therefore, this study aimed to investigate the efficacy of PCCP internal fixation for displaced FNFs in elderly individuals based on imaging. Methods: The clinical data of 32 elderly patients with FNFs treated with PCCP from January 2015 to December 2022 were re Read More
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Enhanced Detection of Colon Diseases via a Fused Deep Learning Model with an Auxiliary Fusion Layer and Residual Blocks on Endoscopic Images
Authors: Rakesh Kumar, Vatsala Anand, Sheifali Gupta, Ahmad Almogren, Salil Bharany, Ayman Altameem and Ateeq Ur RehmanAvailable online: 02 January 2025More LessBackground: Colon diseases are major global health issues that often require early detection and correct diagnosis to be effectively treated. Deep learning approaches and recent developments in medical imaging have demonstrated promise in increasing diagnostic accuracy. Objective: This work suggests that a Convolutional Neural Network (CNN) model paired with other models can detect different gastrointestinal (GI) abno Read More
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