AIMC Topic: Algorithms

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VGX: VGG19-Based Gradient Explainer Interpretable Architecture for Brain Tumor Detection in Microscopy Magnetic Resonance Imaging (MMRI).

Microscopy research and technique
The development of deep learning algorithms has transformed medical image analysis, especially in brain tumor recognition. This research introduces a robust automatic microbrain tumor identification method utilizing the VGG16 deep learning model. Mic...

Identification of an ANCA-associated vasculitis cohort using deep learning and electronic health records.

International journal of medical informatics
BACKGROUND: ANCA-associated vasculitis (AAV) is a rare but serious disease. Traditional case-identification methods using claims data can be time-intensive and may miss important subgroups. We hypothesized that a deep learning model analyzing electro...

Deep learning-based free-water correction for single-shell diffusion MRI.

Magnetic resonance imaging
Free-water elimination (FWE) modeling in diffusion magnetic resonance imaging (dMRI) is crucial for accurate estimation of diffusion properties by mitigating the partial volume effects caused by free water, particularly at the interface between white...

The Application of an Intelligent -Harvesting Device Based on FES-YOLOv5s.

Sensors (Basel, Switzerland)
To address several challenges, including low efficiency, significant damage, and high costs, associated with the manual harvesting of , in this study, a machine vision-based intelligent harvesting device was designed according to its agronomic charac...

Automatic segmentation of MRI images for brain radiotherapy planning using deep ensemble learning.

Biomedical physics & engineering express
This study aimed to develop and evaluate an efficient method to automatically segment T1- and T2-weighted brain magnetic resonance imaging (MRI) images. We specifically compared the segmentation performance of individual convolutional neural network ...

Machine learning analysis of rivaroxaban solubility in mixed solvents for application in pharmaceutical crystallization.

Scientific reports
This study investigates the use of machine learning models to predict solubility of rivaroxaban in binary solvents based on temperature (T), mass fraction (w), and solvent type. Using a dataset with over 250 data points and including solvents encoded...

Classification of NSCLC subtypes using lung microbiome from resected tissue based on machine learning methods.

NPJ systems biology and applications
Classification of adenocarcinoma (AC) and squamous cell carcinoma (SCC) poses significant challenges for cytopathologists, often necessitating clinical tests and biopsies that delay treatment initiation. To address this, we developed a machine learni...

An open source convolutional neural network to detect and localize distal radius fractures on plain radiographs.

European journal of trauma and emergency surgery : official publication of the European Trauma Society
PURPOSE: Distal radius fractures (DRFs) are often initially assessed by junior doctors under time constraints, with limited supervision, risking significant consequences if missed. Convolutional Neural Networks (CNNs) can aid in diagnosing fractures....

Enhanced AMD detection in OCT images using GLCM texture features with Machine Learning and CNN methods.

Biomedical physics & engineering express
Global blindness is substantially influenced by age-related macular degeneration (AMD). It significantly shortens people's lives and severely impairs their visual acuity. AMD is becoming more common, requiring improved diagnostic and prognostic metho...

Application of cloud server-based machine learning for assisting pathological structure recognition in IgA nephropathy.

Journal of clinical pathology
BACKGROUND: Machine learning (ML) models can help assisting diagnosis by rapidly localising and classifying regions of interest (ROIs) within whole slide images (WSIs). Effective ML models for clinical decision support require a substantial dataset o...