ArticlePulmonary medicine2025
A Deep Convolutional Neural Network Model for Lung Disease Detection Using Chest X-Ray Imaging.
Article in Pulmonary medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Multimodal Deep Learning Approaches for Lung Disease Detection: A Review.Medicina (Kaunas, Lithuania) · 2026Review
- Integrating augmentation-aware manifold smoothing and momentum-adjusted loss for handling class imbalance in thoracic disease detection.Frontiers in medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung diseases, including pneumonia and COVID-19, are prevalent globally, necessitating early diagnosis for effective treatment. Medical imaging is widely regarded as an effective method for detecting lung diseases. Numerous researchers have dedicated their efforts to developing advanced detection techniques, significantly contributing to the prevention and management of these conditions. Despite advancements in imaging diagnostic methods, chest radiographs remain pivotal due to their cost-effectiveness and rapid results. This study proposes an automated system for detecting multiple lung diseases in x-ray and CT scans using a customized convolutional neural network (CNN) alongside pretrained models and an image enhancement approach. The dataset used comprises 6400 images sourced from Kaggle, categorized into three classes: pneumonia, COVID-19, and normal. To address dataset imbalance, data augmentation techniques were applied. The model includes preprocessing and classification stages, achieving high performance metrics: 96% precision, 95.33% recall, 95.66%
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.