Chest X-ray (CXR) is widely used to diagnose conditions affecting the chest, its contents, and nearby structures. In this study, we a private data set containing 1630 CXR images with disease labels; most of were disease-free, but others contained multiple sites abnormalities. Here, deep convolutional neural network (CNN) models extract feature representations identify possible diseases in these images. We also transfer learning combined large open-source image sets resolve problems...

Convolutional neural networks (CNNs) have shown promise in accurately diagnosing coronavirus disease 2019 (COVID-19) and bacterial pneumonia using chest X-ray images. However, determining the optimal feature extraction approach is challenging. This study investigates use of fusion-extracted features by deep to improve accuracy COVID-19 classification with radiography. A Fusion CNN method was developed five different learning models after transferred extract image (Fusion CNN). The combined...

Early detection of prostate cancer (PCa) and benign prostatic hyperplasia (BPH) is crucial for maintaining the health well-being aging male populations. This study aims to evaluate performance transfer learning with convolutional neural networks (CNNs) efficient classification PCa BPH in transrectal ultrasound (TRUS) images. A retrospective experimental design was employed this study, 1380 TRUS images 1530 BPH. Seven state-of-the-art deep (DL) methods were as classifiers applied popular CNN...

This study focuses on overcoming challenges in classifying eye diseases using color fundus photographs by leveraging deep learning techniques, aiming to enhance early detection and diagnosis accuracy. We utilized a dataset of 6392 across eight disease categories, which was later augmented 17,766 images. Five well-known convolutional neural networks (CNNs)-efficientnetb0, mobilenetv2, shufflenet, resnet50, resnet101-and custom-built CNN were integrated trained this dataset. Image sizes...

Abstract Convolutional deep learning models have shown comparable performance to radiologists in detecting and classifying thoracic diseases. However, research on rib fractures remains limited compared other abnormalities. Moreover, existing primarily focus using frontal chest X‐ray (CXR) images. To address these gaps, the authors utilised EDARib‐CXR dataset, comprising 369 829 oblique CXRs. These X‐rays were annotated by experienced radiologists, specifically identifying presence of...

Intracerebral hemorrhage (ICH) is a common and severe neurological disorder associated with high morbidity mortality rates. Despite extensive research into its pathology, there are no clinically approved neuroprotective treatments for ICH. Increasing evidence has revealed that inflammatory responses mediate the pathophysiological processes of brain injury following Experimental ICH was induced by direct infusion 100 μL fresh (non-heparinized) autologous whole blood right basal ganglia...

Coronary artery disease (CAD) remains the leading cause of death worldwide. Currently, cardiac multi-detector computed tomography (MDCT) is widely used to diagnose CAD. The purpose in this study identify informative and useful predictors from left ventricular (LV) early CAD patients using MDCT images.Study groups comprised 42 subjects who underwent a screening health examination, including laboratory testing angiography by 64-slice angiography. Two geometrical characteristics one image...

Dental panoramic imaging plays a pivotal role in dentistry for diagnosis and treatment planning. However, correctly positioning patients can be challenging technicians due to the complexity of equipment variations patient anatomy, leading errors. These errors compromise image quality potentially result misdiagnoses.This research aims develop validate deep learning model capable accurately efficiently identifying multiple dental imaging.This retrospective study used 552 images selected from...

Background: Ultrasound imaging has become one of the most widely utilized adjunct tools in breast cancer screening due to its advantages. The computer-aided detection ultrasound is rapid development via significant features extracted from images. Objectives: main aim was identify image that can facilitate reasonable classification images between malignant and benign lesions. Patients Methods: This research a retrospective study which 85 cases (35 [positive group] 50 [negative with diagnostic...

PURPOSE:A novel diagnostic method using the standard deviation (SD) value of apparent diffusion coefficient (ADC) by diffusion-weighted (DWI) magnetic resonance imaging (MRI) is applied for differential diagnosis primary chest cancers, metastatic tumors and benign tumors. MATERIALS AND METHODS:T his retrospective study enrolled 27 patients (20 males, 7 female; age, 15–85; mean 68) who had thoracic mass lesions in last three years underwent an MRI examination at our institution. In total, 29...

According to the Health Promotion Administration in Ministry of and Welfare statistics Taiwan, over ten thousand women have breast cancer every year. Mammography is widely used detect cancer. However, it limited by operator's technique, cooperation subjects, subjective interpretation physician. It results inconsistent identification. Therefore, this study explores use a deep neural network algorithm for classification mammography images. In experimental design, retrospective was collect...

Background/Objectives: Breast cancer is a leading cause of mortality among women in Taiwan and globally. Non-invasive imaging methods, such as mammography ultrasound, are critical for early detection, yet standalone modalities have limitations regard to their diagnostic accuracy. This study aims enhance breast detection through cross-modality fusion approach combining ultrasound imaging, using advanced convolutional neural network (CNN) architectures. Materials Methods: images were sourced...

Background/Objectives: Spinal conditions, such as fractures and herniated intervertebral discs (HIVDs), are often challenging to diagnose due overlapping clinical symptoms the difficulty in assessing their functional impact. Accurate differentiation between these conditions is crucial for effective treatment, particularly context of preoperative anesthesia evaluation, where understanding underlying condition can influence planning pain management. Methods Materials: This study presents a...

Background and Objectives: Chest X-ray (CXR) images are commonly used to diagnose respiratory cardiovascular diseases. However, traditional manual interpretation is often subjective, time-consuming, prone errors, leading inconsistent detection accuracy poor generalization. In this paper, we present deep learning-based object methods for automatically identifying annotating abnormal regions in CXR images. Methods: We developed tested our models using disease-labeled location-bounding boxes...

Abstract Purpose Early detection of prostate cancer (PCa) and benign prostatic hyperplasia (BPH) is crucial for maintaining the health well-being aging male populations. This study aims to evaluate performance transfer learning with convolutional neural networks (CNNs) efficient classification PCa BPH in transrectal ultrasound (TRUS) images. Methods A retrospective experimental design was employed this study, 1,380 TRUS images 1,530 BPH. Seven state-of-the-art deep (DL) methods were as...

Several deep learning-based object detection techniques in medical imaging have been proposed. Chest X-rays are widely used for detecting thorax diseases due to the convenience and low radiation dose compared Computed Tomography (CT). However, research on rib fracture chest is still inadequate. Most of primarily focused frontal CXR some lateral CXR. No study oblique view has previously Due overlapping characteristic human ribs, can help radiologists recognize fractured ribs that blocked...

The function of left ventricle (LV) was evaluated by ejection fraction (EF) computed from minimum systolic and maximum diastolic phase CTA. EF took contrast enhanced LV into consideration during phases diastolic. In this study, the volume ratio (sVr) developed used to analyze LV. sVr significant difference between positive negative groups as well EF. On other hand, high correlated with

BACKGROUND: Dividing liver organs or lesions depicting on computed tomography (CT) images could be applied to help tumor staging and treatment. However, most existing image segmentation technologies use manual semi-automatic analysis, making the analysis process costly time-consuming. OBJECTIVE: This research aims develop apply a deep learning network architecture segment tumors automatically after fine tuning parameters. METHODS AND MATERIALS: The medical imaging is obtained from...