arXiv Computer Vision
Extending the Horizon of Early Diagnosis: Lung Cancer Prediction with Vision Transformers
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arXiv AI
4d ago
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.
arXiv AI
Jul 9
Vision Foundation Models in Radiology: A Scoping Review of Data, Methodology, Evaluation and Clinical Translation
arXiv:2607. 07219v1 Announce Type: cross Abstract: Vision foundation models (VFMs) are increasingly being developed for radiological imaging, yet their definition, development and evaluation remain heterogeneous.
Hugging Face Trending Papers
Jul 9
CT-CLIP Representations for Multimodal Lung Cancer Survival Prediction
Accurate prognosis prediction is important for treatment planning in lung cancer, but deep learning-driven survival modelling is often limited by the scarcity of curated imaging cohorts with reliable outcome data. This study evaluates whether representations from a domain-specific foundation model can be used for multimodal survival prediction in data-constrained clinical settings.
arXiv Machine Learning
Jul 22
Tumor-anchored deep feature random forests for out-of-distribution detection in lung cancer segmentation
arXiv:2512. 08216v4 Announce Type: replace-cross Abstract: Accurate segmentation of lung tumors from 3D computed tomography (CT) scans is essential for automated treatment planning and response assessment.
arXiv Machine Learning
Jun 4
Radiomic Feature Selection Using Gradient Loss of Deep Neural Network for Lung Cancer Stage Detection
arXiv:2606. 04453v1 Announce Type: cross Abstract: Radiomics enables extraction of quantitative imaging biomarkers from medical images and has become an important tool for computer-aided cancer diagnosis.