Tag: convolutional neural networks

A review in Cell Division evaluates AI and machine learning models for pancreatic cancer diagnosis, highlighting their high reliability in ...

Researchers have introduced LCS-Net, a lightweight collaborative neural network that executes simultaneous real-time object detection and semantic segmentation for autonomous ...

Researchers have developed a dual-domain deep learning network that accurately classifies electroencephalogram artifacts while distinguishing routine movements from diagnostic temporal ...

Researchers developed a compact road-object detector coupling high-resolution feature alignment and quality-aware assignment to improve small-object detection accuracy on benchmark ...

Researchers introduce Global Collaborative Learning to align object localization and classification by mining sub-hard training samples and enabling cross-task feature ...

Researchers have introduced ReCAN, a lightweight convolutional neural network that achieves high medical image classification accuracy with substantially reduced computational ...

Researchers propose a reinforcement learning method that automatically selects which layers of a pretrained neural network to fine-tune for medical ...

A novel dual-branch neural network called EEG-DBNet simultaneously decodes temporal dynamics and spectral representations to significantly enhance motor imagery classification ...

Researchers have developed a privacy-preserving framework called FLICKER that uses CKKS homomorphic encryption to detect and correct global class imbalance ...

Deep learning models for tuberculosis screening suffer substantial diagnostic sensitivity losses and increased calibration error when evaluated on chest radiographs ...

A lightweight MobileNet model achieved 95.76 percent accuracy in identifying tomato leaf diseases across ten classes, offering an efficient diagnostic ...

An empirical evaluation of ten convolutional neural network architectures across three imaging modalities reveals that ImageNet pretraining gains do not ...

A systematic benchmarking study demonstrates that DenseNet-121 paired with the Adam optimizer achieves exceptional diagnostic accuracy in detecting COVID-19 from ...

A survey published in Artificial Intelligence Review introduces a formal framework linking diverse computer vision architectures and training objectives through ...

Multispectral imaging flow cytometry with machine learning identified pollen in monofloral and polyfloral honeys with results comparable to microscopy in ...

A fully convolutional network with layer-wise fine-tuning classifies malware families with over 99% accuracy across three datasets and processes obfuscated ...

A new narrative review finds AI tools improving diagnostic sensitivity and workflow efficiency in radiology while warning of demographic bias, ...

Researchers report an AI model combining CNN feature extraction and a spiking neural network that classifies microscopic fabric defects with ...

A class-adaptive ensemble of three neural networks classifies brain tumors in MRI scans with 99.39 percent benchmark accuracy, validated by ...

A prospective Indian study reports a deep-learning ensemble that classifies thyroid FNAC images into 2023 Bethesda categories with 96 percent ...