The paper introduces the Exponential Pixelating Integral (EPI) transform, which enhances pixel intensities in chest X‑rays to mitigate low‑contrast and noise issues. The enhanced images are polar‑transformed and represented using locally invariant Mandelbrot and Julia fractal geometries, producing dual fractal features. These features are classified with non‑parametric multivariate adaptive regression splines, achieving high accuracy (98.46–99.45%) and F1 scores (96.53–98.10%) on large benchmark datasets.
By Naveenraj Kamalakannan, Sri Ram Macharla, M Kanimozhi, M S Sudhakar
arXiv:2607. 05628v1 Announce Type: cross Abstract: Accurate and efficient classification of thoracic diseases in chest X-ray (CXR) images is crucial for timely diagnosis and treatment.
By Mohammad S. Majdi, Jeffrey J. Rodriguez
arXiv:2608.21571v1 Announce Type: new
Abstract: Lung cancer remains a leading cause of cancer-related mortality worldwide, and early diagnosis is critical for improving survival. However, early-stage...
By Olivera Kotevska, Ian Goethert, Michael McGee, Maria Mahbub, Sean R. Wilkinson, Rowena Yip, Myvizhi Esai Selvan, Zeynep H. Gumus, Claudia Henschke, Robert J. Klein, Providencia Morales, Samuel M Aguayo, Ioana Danciu, Mayanka Chandrashekar
arXiv:2607. 04478v1 Announce Type: cross Abstract: Automated chest X-ray classification remains challenging due to severe class imbalance, co-occurring pathologies, and the loss of localized features in conventional architectures.
By Moshiur Rahman, Shafqat Alam, Tasnia Binte Mamun
arXiv:2607. 26333v1 Announce Type: cross Abstract: Chest X-ray (CXR) machine learning relies heavily on automated evaluation using reference standards that aim to approximate clinical judgment.
By Panagiotis Fytas, Ian Selby, Clemens Karner, Judith Babar, Simon Baker, Jake Beckford, Timothy J. Sadler, Shahab Shahipasand, Arthikkaa Thavakumar, John Li Chen, Alex Sawer, Michael Roberts, Jonathan Weir-McCall, J. H. F. Rudd, Carola-Bibiane Sch\"onlieb, Anna Korhonen, Anna Breger
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.
By Sarmad Khan, Basim Azam, Arslan Shaukat
arXiv:2509. 19671v3 Announce Type: replace Abstract: Public datasets of Chest X-Rays (CXRs) have long been a popular benchmark for developing machine learning (ML) computer vision models in healthcare.
By Andrew Wang, Jiashuo Zhang, Michael Oberst
arXiv:2607. 00975v1 Announce Type: cross Abstract: Chest X-ray multi-label classification is a core task in intelligent medical imaging diagnosis.
By Tong Shao, Hongshun Ling, Li Zhang, Jinjing Wu, Junke Wang, Yuan Gao, Fang Wang
arXiv:2608.30467v1 Announce Type: new
Abstract: Deep-learning models can achieve strong chest X-ray (CXR) classification performance without establishing whether their predictions predominantly rely...
By Abdullah Al Mamun, Md. Nasif Osman Khansur, Md Ashraful Hossen Akash, Md. Kishor Morol, Tze Hui Liew
The CXR‑LT 2026 Challenge introduces a multi‑center, long‑tailed chest X‑ray classification benchmark with over 145,000 radiologist‑annotated images from PadChest and NIH datasets. It defines two core tasks: robust multi‑label classification on 30 known classes and open‑world generalization to 6 unseen rare disease classes. The paper outlines data collection, annotation, solution strategies, and evaluates performance across head‑vs‑tail, calibration, and cross‑center gaps, noting that vision‑language models improve in‑distribution and zero‑shot performance but rare‑finding detection under multi‑center shift remains difficult.
By Hexin Dong, Yi Lin, Pengyu Zhou, Fengnian Zhao, Alan Clint Legasto, Juno Cho, Dohui Kim, Justin Namuk Kim, Mingeon Kim, Sunwoo Kwak, Gabriel Moy\`a-Alcover, Ky Trung Nguyen, Thanh-Huy Nguyen, Ha-Hieu Pham, Huy-Hieu Pham, Huy Le Pham, Nikhileswara Rao Sulake, Aina Tur-Serrano, Ruichi Zhang, Ang Zu, Adam E. Flanders, Zhiyong Lu, Ronald M. Summers, Mingquan Lin, Hao Chen, Yuzhe Yang, George Shih, Yifan Peng
arXiv:2607. 13936v1 Announce Type: cross Abstract: Arrhythmogenic left ventricular cardiomyopathy is a genetic myocardial disease difficult to diagnose due to the lack of gold standard criteria.
By Brunnhilde Ponsi (Nantes Universit\'e, CHU Nantes, Nantes, France, CRCI2NA, INSERM UMR 1307, Nantes, France), Thomas Carlier (Nantes Universit\'e, CHU Nantes, Nantes, France, CRCI2NA, INSERM UMR 1307, Nantes, France), Lara Marteau (Nantes Universit\'e, CHU Nantes, Nantes, France, Cardiology Department, INSERM UMR 1307, CIC 1413, l'institut du Thorax, Nantes, France), Aur\'elien Monnet (Siemens Healthineers France, Courbevoie, France), Thomas Eug\`ene (Nantes Universit\'e, CHU Nantes, Nantes, France, CRCI2NA, INSERM UMR 1307, Nantes, France), Jean-Michel Serfaty (Nantes Universit\'e, CHU Nantes, Nantes, France, Radiology Department, l'institut du Thorax, Nantes, France), Nicolas Piriou (Nantes Universit\'e, CHU Nantes, Nantes, France, Cardiology Department, INSERM UMR 1307, CIC 1413, l'institut du Thorax, Nantes, France), Hatem Necib (Nantes Universit\'e, CHU Nantes, Nantes, France, CRCI2NA, INSERM UMR 1307, Nantes, France)
Deep-learning models can achieve strong chest X-ray (CXR) classification performance without establishing whether their predictions predominantly rely on pulmonary image content. This study evaluates...