ArticleScientific reports2026
Classification of pulmonary disorders using pulmonary network with explainable AI framework from chest multi-modal images.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pulmonary disease (PD) is one of the most common diseases worldwide. To detect the PDs, several tests like RT-PCR, CBC, and antigen tests were used. But these are time-consuming processes. Therefore, chest radiographs are used to classify the PDs, but this may lead to false results due to similar lung lesions and nodules present in the chest radiograph. So, deep learning-based models were introduced, but these conventional models were less efficient. As a result, we propose the 30-layer novel PD classification network termed as PULMONET to classify the PDs from multi-modal chest images. In this, we have used 9917 chest multi-modal images to classify PDs into binary and ten classes. The model is trained with 50 epochs, 64 batch sizes, Adam optimizer and 5-fold cross-validation is used. In 5 folds, four folds were used for training and remaining one fold was used for validation to avoid overfitting and provide efficient data for training. The proposed model provides the highest accuracy of 99.17% for binary and 90.70% for ten-class classification scenarios respectively. Finally, we applied heat maps to initial and deeper layers to study the network behavior to acknowledge the decision-making of the PULMONET. The experimental findings show significant increases across performance parameters and good classification accuracy, demonstrating the resilience of the technique. In the proposed 30-layer PULMONET, the diagnostic accuracy of medical picture analysis is generally increased. Lastly, we investigated network behavior and the issue of misclassification by applying the t-SNE and activation maps to the network’s initial and deeper layers. These activations show clear clusters of the correctly classified and misclassified classes. Also, we used a custom segmentation map to view the best discriminative features that are more useful for the decision made by the model. The datasets, codes, and figures that are used in this work is available in: https://www.kaggle.com/datasets/sahebgoudkaraddi/pulmonary-diseases-datasetsct-cxr
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.