Evidence map›Paper›PMID 41960513›Full record

ArticleBioinformation2026

Multimodal AI for pneumonia and lung cancer classification using x-ray and HRCT.

Chauhan Pradip, Chauhan Girish, Chauhan Bhoomika, Vaza Jayesh, Ratanpara Lalit, Mehra Simmi

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioinformation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

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.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Chauhan PradipDepartment of Anatomy, All India Institute of Medical Sciences, Rajkot, Gujarat, India.
Chauhan GirishOral Pathology and Maxillofacial Surgery, Government Dental College, Jamnagar, Gujarat, India.
Chauhan BhoomikaDepartment of Obstetrics and Gynaecology, Bhagyoday Medical College, Kadi, Gujarat, India.
Vaza JayeshDepartment of Orthopaedics, Narendra Modi Medical College and Hospital, Ahmedabad, Gujarat, India.
Ratanpara LalitDepartment of Anatomy, All India Institute of Medical Sciences, Rajkot, Gujarat, India.
Mehra SimmiDepartment of Anatomy, All India Institute of Medical Sciences, Rajkot, Gujarat, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chest X-ray and HRCT are essential for diagnosing pneumonia and lung cancer, but their accuracy is limited. Hence, DeepScan, a multimodal AI combining CNNs trained on both imaging types, was developed using public datasets. The architecture included resnet-50 for X-rays, densenet-121 for HRCT and a late-fusion network. DeepScan outperformed single-modality models, achieving 94.6% accuracy, 95.2% sensitivity, 93.9% specificity and an AUC of 0.97 on 2,000 test patients. Multimodal integration reduced false negatives for early-stage lung cancer and improved differentiation from pneumonia, supporting earlier intervention and potentially enhancing clinical workflows.

Indexed as

Artificial intelligenceChest X-Raydeep learningHRCTlung cancermultimodal imagingpneumonia

Identifiers

PMID41960513
PMCPMC13058361

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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.