COVID-19 diagnosis using AI
New papers in covid-19 diagnosis using ai, the code and data they release, and the open problems their authors admit. Rebuilt every day from OpenAlex and arXiv.
New papers
Explainable AI-Based Pulmonary Nodule Classification from Computed Tomography Images with an End-to-End Clinical Decision Support Interface
This paper presents an explainable, end-to-end CAD system for pulmonary nodule classification that addresses both shortcomings.
A Hybrid Enhancement Framework for Chest X-Ray Images Using CLAHE, Haar Wavelet, and Denoising Filters: A Quality Metrics Analysis
Chest radiographic imaging is one of the more frequently used diagnostic techniques in the diagnostic investigation of diseases.
A Trustworthy Hybrid AI Framework for Heart Disease Prediction Using Clinical Features and Explainable Deep Learning
This paper proposes a trustworthy hybrid artificial intelligence framework that combines conventional machine learning algorithms, a multilayer perceptron (MLP), probability-level fusion, and explainable artificial intelligence (XAI).
The Role of Artificial Neural Networks Techniques in the Fight Against COVID-19: A Review
This review presents an overview of the current public health emergency and the development of Artificial Neural Network techniques incorporation with medical imaging, such as chest computed tomography images and chest X-rays.
Class weights miscalibrate chest X-ray classifiers by a predictable amount: calibration, prevalence shift and subgroup noise in a reproducible ChestX-ray14 baseline
Class-weighted binary cross-entropy is a common default for multi-label chest X-ray classification, and models are usually judged by mean AUROC alone.
A Distributed Vision Transformer-Based Deep Learning Framework for Early Detection and Classification of Lumbar Disc Degeneration
Lumbar disc degeneration (LDD) is a principal structural cause of chronic low back pain.
ANALISIS PERBANDINGAN CNN DENGAN COST-SENSITIVE LEARNING UNTUK DETEKSI TUBERKULOSIS PARU PADA CITRA X-RAY THORAX
Tuberculosis (TB) remains one of the heaviest infectious disease burdens in Indonesia, with an estimated 1.09 million cases and 125,000 deaths each year, while access to radiologists in primary health facilities is limited.
Multicenter clinical validation of a deep learning model for automated diagnosis and classification of aortic dissection
Aortic dissection (AD) is a life-threatening cardiovascular emergency requiring rapid and accurate diagnosis.
Lung Disease Detection
Pneumonia, lung cancer and chronic obstructive pulmonary disease (COPD) cause many deaths worldwide, and patients do better when these illnesses are found early.
Deteksi Dini Pneumonia Berbasis Gejala Klinis Menggunakan Random Forest: Early Detection of Pneumonia Based on Clinical Symptoms Using Random Forest
Pneumonia merupakan salah satu penyakit pernapasan akut dengan tingkat morbiditas dan mortalitas yang tinggi, terutama pada anak-anak dan lansia.
Latent-aligned scalable interpolant transformer for controllable chest X-ray synthesis
We introduce LatAlignCXR, which represents anatomical layout, pathology location, and residual appearance within a structured latent space.
Automated Pneumonia Detection from Chest X-Rays: Overcoming Data Imbalance with VGG16
In diagnosing and screening for pneumonia, extensive research is underway to avoid deliberate delays caused by the shortage of human radiologists.
ClinX ‐Net: Multimodal Visual–Language Feature Fusion for Chest X‐Ray Report Generation
To address these limitations, we propose ClinX‐Net, a multimodal framework for chest X‐ray report generation that combines hierarchical visual representation learning, disease‐aware semantic encoding, and language‐model‐based text generation.
Degree bias predicts, outperforms and improves machine learning phage-host interaction predictions
Machine learning (ML) models predicting phage-host interactions are widely used to prioritize phages for phage therapy.
Transferability of cost-sensitive threshold policies in pneumoniamnist: Calibration, resampling, and image-integrity sensitivity
Background: Validation-selected thresholds may fail to retain their apparent error-cost advantage across imaging benchmark partitions, even with satisfactory discrimination.
Cross-Domain Slow Wave Sleep Detection from Single-Lead EEG in Pediatric Congenital Heart Disease Recovery
Slow wave sleep (SWS) is vital for neural recovery and cardiovascular stability in children following congenital heart disease (CHD) surgery.
Detection of Newcastle Disease in Chicken using Xception-XGBoost Framework
Newcastle disease or Newcastle (ND) is a serious viral disease of poultry.
Causal-prior-constrained deep learning for rapid-intensification warning of western North Pacific tropical cyclones
Here we introduce CausalVisionRI, a causal-prior-constrained deep-learning system for 24-h RI warning in the western North Pacific.
Anatomical Prior-Guided Routing for 3D Lung Tumor Segmentation
To address this limitation, we propose an Anatomical Prior-guided Routing Module (APRM) for lung tumor segmentation.
PathCowork: Learning from real-world pathology practice to build AI copilots
Here we introduce PathCowork, a framework that turns pathologists' narrated examinations into supervision for visual grounding and procedures that guide diagnostic AI agents.
Released code and data
Self-training for cross-centre coronary vessel segmentation in X-ray angiography using pre-contrast frame subtraction
Apart from differences in equipment, the labels of the largest public dataset (ARCADE) are incomplete, as only the segments defined in the SYNTAX score are annotated.
Scalable drift monitoring in medical imaging AI
The code for MMC+ is publicly available (Link: https://github.com/FJDorfner/MedImaging-ModelDriftMonitoring/tree/MMC_plus ), promoting broader adoption of AI solutions in clinical settings.
What Matters for Lightweight Multi-Label Chest X-Ray Classification? A Controlled Multi-Seed Study of Attention, Loss, Width and Resolution
Code, trained weights and machine-readable results are released.
Interpretability Failure Modes of Computer Vision Triage Classifiers Under computed Camera Occlusion in Low-Resource Clinics: A Healthcare Robustness Evaluation
Because no field data are available, the study is framed as a design-based robustness evaluation: a before-after-control-impact (BACI) estimator is characterised under a declared variance structure with intraclass correlation 0.64, between-cluster SD 6.0 and residual SD 4.5, across cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points, with 2000 replications per
Segment–Enhance–Extract–Reweight Framework with a Learnable Feature-reweighting Gate for Multi-label Thoracic Disease Classification on Chest Radiographs
accuracy at IoU 0.5 on held-out boxes from 0.242 to 0.359.Zero-shot evaluation on CheXpert and PadChest and a cross-corpus transfer matrix show the gain survives domain shift.All macro averages are computed from unrounded per-class values.Code, the exact split, comparator configurations and per-seed outputs are released at https://github.com/samrootkhan9182-oss/SEER-CXR.
A topology-aware visual fusion framework for patient-level emphysema classification from chest CT images
The anonymized dataset and source code are available at: https://github.com/muhammedtalo/Emphysema .
SCCL-DT: Semantic-Conflict-Aware Contrastive Learning with Dual-Rule Teacher Distillation for Zero-Shot Chest X-Ray Diagnosis
Code will be made publicly available soon.
From Adults to Children: Benchmarking Foundation Models for Chest X-Ray Classification under Domain Shift
To the best of our knowledge, this is the first benchmark to compare multiple foundation models under a common adult-to-pediatric evaluation setting while also analyzing the effect of adaptation depth.
Single-Pass Decision Scores Enable Rapid Classification with Adjustable Thresholds in Medical Multimodal Large Language Models
This simple post-hoc method turns medical MLLMs into fast classifiers with adjustable operating points. Code: https://github.com/TruhnLab/llm-threshold-calibration
Towards Medical Image Dataset Distillation: A Progressive Trajectory Matching and Dynamic Diversity Enhancing Method
Guided by the analysis results, we propose a novel progressive and diverse dataset distillation (PD$^{3}$) method, which incorporates (1) a progressive trajectory matching paradigm that progressively unrolls the student training trajectory to extend the matching horizon, while adaptively selecting optimal expert matching trajectory to ensure stable and convergent distillation optimization; and (2) a dynamic diversity enhancing module that alleviates the redundancy of synthetic images by enhancing intra-class distribution and breaking redundancy accumulation.
Open problems authors admit
Explainable Artificial Intelligence for Heart Disease Prediction: A SHAP and LIME-Based Approach — A Review
Challenges, limitations and future research directions are also discussed.
Integrated Framework for Pneumonia Prediction from Chest X-ray Images: Comparative Analysis of Machine Learning and Deep Learning Models Leveraging Transfer Learning Techniques
The research paper discusses the nuances of model selection, parameter tuning, and the interpretability of results, aiming to guide future research endeavors in the realm of computer-aided diagnosis for respiratory diseases.
A secure and explainable hybrid CNN-transformer framework for trustworthy skin lesion classification
The framework achieved an accuracy of 98.95% and precision of 98.05% on a stratified held-out test set drawn from the same combined HAM10000 and ISIC 2019 training distribution, achieving state-of-the-art performance relative to standard baselines; evaluation on a fully independent, out-of-distribution dataset was not performed in this study and is identified as a direction for future work.
The Role of X-ray Imaging in Tuberculosis Diagnosis and Management: A Comprehensive Review
This comprehensive review explores the evolution, current applications, and future directions of X-ray imaging in TB care.
Diagnostic performance of artificial intelligence in pediatric diseases: a systematic review and meta-analysis
Therefore, future research should prioritize external validation across diverse datasets to ensure reliability and support integration of AI-based diagnostic tools into pediatric clinical practice.
Artificial Intelligence for Medical Image Analysis in Low-Resource Settings
Future research should prioritise representative local evaluation, intended-user assessment, demonstrated resource suitability, and reproducible research practices.
Artificial Intelligence for Intelligent and Privacy-Preserving Healthcare: A Survey of Medical Image Analysis, Multimodal Diagnosis, Semi-Supervised Learning, and Personalized Healthcare Systems
Future directions include privacy-preserving multimodal learning, reduced reliance on annotations, explainable and robust models, integration of imaging with clinical records, and multi-site clinical validation.
A Review of Lightweight CNN Architectures and Transfer Learning Methods for Automated Pneumonia Detection
Future research should focus on developing efficient, interpretable, and clinically reliable models that can be deployed in resource-constrained healthcare environments.
Explainable Artificial Intelligence for Computer Vision: A Comprehensive Study on Interpretability Techniques, and Applications
The paper concludes by discussing open challenges, including explanation faithfulness, computational overhead, bias propagation, and the evaluation of explainable vision transformers, and outlines future research directions toward standardized, responsible, and human-centered explainable computer vision systems.
Diagnostic performance of an open-source artificial intelligence model for detecting chest infections on after-hours emergency department chest radiographs in Singapore
Limitations include single-radiologist reporting with radiographic findings as surrogate for infection, reflecting real-world ED practice.Conclusion CheXNeXt demonstrated favourable performance and potential as a triage support tool to alleviate ED CXR workloads.