arXiv Computer Vision
Parallel Training Using a CNN-DNN Architecture for Accelerated Development of Diagnostic Models
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arXiv AI
Aug 28
A Comprehensive Comparison of Deep Learning Architectures for COVID-19 Classification on CT & X-ray Imagery
The article presents a comparative study of convolutional neural network (CNN) architectures for classifying COVID-19 from healthy lung images using CT and X‑ray scans. Multiple pre‑trained models—including VGG, DenseNet, ResNet, MobileNet, Xception, Inception, EfficientNet, and NasNet—were evaluated on two X‑ray and two CT datasets. ResNet and VGG achieved the highest accuracies, ranging from 95% to 98%, outperforming previous reports in the literature.
arXiv Machine Learning
Jul 30
Classification of Disease from Lungs X-ray Images using VGG16, VGG19 and ResNet50 Models
arXiv:2607. 26580v1 Announce Type: cross Abstract: With the increase in the number of cases related to respiratory diseases, there is an urgent need to detect them early and diagnose them accurately.
Hugging Face Trending Papers
Jun 21
Multi-cancer detection using a computationally efficient CNN with transfer learning
This study introduces a computationally efficient convolutional neural network (CNN) architecture enhanced with transfer learning for multi-cancer detection using biomedical images. The proposed lightweight CNN model is designed to reduce computational complexity while maintaining high classification performance, making it suitable for deployment in resource-constrained environments.
arXiv AI
Aug 12
Energy and Performance Benchmarking of Deep Learning Models for Breast Cancer Detection
arXiv:2608. 09996v1 Announce Type: cross Abstract: Recent advances in machine learning have greatly improved breast cancer detection, enabling more accurate and timely diagnosis.
arXiv AI
Aug 28
Pixel Wised Lesion Prediction on COVID-19 CT Imagery: A Comparative Analysis of Automated Image Segmentation Architectures
The study evaluates four deep‑learning segmentation architectures—Unet, PSPNet, Linknet, and FPN—paired with six pre‑trained encoders to predict COVID‑19 lesions in CT images. Experiments on three COVID‑19 CT datasets show high accuracy, achieving a maximum binary F1‑score of 98% and multi‑class F1‑scores of 75% and 77%. The work aims to provide a standardized performance benchmark for medical image segmentation and a reference for other imaging scenarios.
arXiv Machine Learning
Aug 4
RadYOLO: Computationally Efficient 3D Object Detection and Segmentation in CT and MRI
arXiv:2608. 00508v1 Announce Type: cross Abstract: Object detection and segmentation in three-dimensional medical images is a very active area of research.