AI Medical Compendium Journal:
BMC medical imaging

Showing 51 to 60 of 252 articles

Effective BCDNet-based breast cancer classification model using hybrid deep learning with VGG16-based optimal feature extraction.

BMC medical imaging
PROBLEM: Breast cancer is a leading cause of death among women, and early detection is crucial for improving survival rates. The manual breast cancer diagnosis utilizes more time and is subjective. Also, the previous CAD models mostly depend on manma...

Liver fibrosis stage classification in stacked microvascular images based on deep learning.

BMC medical imaging
BACKGROUND: Monitoring fibrosis in patients with chronic liver disease (CLD) is an important management strategy. We have already reported a novel stacked microvascular imaging (SMVI) technique and an examiner scoring evaluation method to improve fib...

HistoNeXt: dual-mechanism feature pyramid network for cell nuclear segmentation and classification.

BMC medical imaging
PURPOSE: To develop an end-to-end convolutional neural network model for analyzing hematoxylin and eosin(H&E)-stained histological images, enhancing the performance and efficiency of nuclear segmentation and classification within the digital patholog...

Automatic image generation and stage prediction of breast cancer immunobiological through a proposed IHC-GAN model.

BMC medical imaging
Invasive breast cancer diagnosis and treatment planning require an accurate assessment of human epidermal growth factor receptor 2 (HER2) expression levels. While immunohistochemical techniques (IHC) are the gold standard for HER2 evaluation, their i...

Novel transfer learning based bone fracture detection using radiographic images.

BMC medical imaging
A bone fracture is a medical condition characterized by a partial or complete break in the continuity of the bone. Fractures are primarily caused by injuries and accidents, affecting millions of people worldwide. The healing process for a fracture ca...

Interpretable machine learning model for predicting clinically significant prostate cancer: integrating intratumoral and peritumoral radiomics with clinical and metabolic features.

BMC medical imaging
BACKGROUND: To develop and validate an interpretable machine learning model based on intratumoral and peritumoral radiomics combined with clinicoradiological features and metabolic information from magnetic resonance spectroscopy (MRS), to predict cl...

Comparison and analysis of deep learning models for discriminating longitudinal and oblique vaginal septa based on ultrasound imaging.

BMC medical imaging
BACKGROUND: The longitudinal vaginal septum and oblique vaginal septum are female müllerian duct anomalies that are relatively less diagnosed but severely fertility-threatening in clinical practice. Ultrasound imaging is commonly used to examine the ...

Enhancing classification of active and non-active lesions in multiple sclerosis: machine learning models and feature selection techniques.

BMC medical imaging
INTRODUCTION: Gadolinium-based T1-weighted MRI sequence is the gold standard for the detection of active multiple sclerosis (MS) lesions. The performance of machine learning (ML) and deep learning (DL) models in the classification of active and non-a...

Novel neural network classification of maternal fetal ultrasound planes through optimized feature selection.

BMC medical imaging
Ultrasound (US) imaging is an essential diagnostic technique in prenatal care, enabling enhanced surveillance of fetal growth and development. Fetal ultrasonography standard planes are crucial for evaluating fetal development parameters and detecting...