AZoM

Why Deep Learning De-Noising is a Game Changer for X-Ray Technology

Innovative X-ray technology from Hamamatsu Photonics improves safety standards in food and non-destructive testing through ...
eLife

High-content high-resolution microscopy and deep learning-assisted analysis reveals host and bacterial heterogeneity during Shigella infection

High-content high-resolution microscopy coupled with deep learning-based analysis reveals novel phenotypes at the single-cell level during Shigella infection.
IEEE

HAM10000 Skin Disease Detection And Classification Using Deep Learning

Abstract: This study uses the HAM10000 dataset to present an experimental method of classifying skin diseases using a hybrid model that combines CNN and K-Nearest Neighbours (KNN). The suggested model ...
Frontiers

Regional CSF volume quantification using deep learning for comparative analysis of brain atrophy in frontotemporal dementia subtypes

Introduction: Frontotemporal dementia (FTD) encompasses heterogeneous clinical syndromes, and distinguishing its subtypes using imaging remains challenging. Methods: We developed a deep learning model ...
Frontiers

MLVI-CNN: a hyperspectral stress detection framework using machine learning-optimized indices and deep learning for precision agriculture

Department of Electronics Communication Engineering, SRM Institute of Science and Technology, Vadapalani Campus, Chennai, India Introduction: Early and accurate detection of crop stress is vital for ...
IEEE

A Lightweight Spatial–Spectral Deformable CNN for UAV Hyperspectral Image Classification

Abstract: In recent years, uncrewed aerial vehicle (UAV) technology has shown great potential for application in hyperspectral image (HSI) classification tasks due to its advantages of flexible ...