Back to search

Article

Truncating the EfficientNet-B0 for Computer Aided Diagnosis of Tuberculosis

2026-05-11

Abstract excerpt

This study investigates the effectiveness of truncating the EfficientNet-B0 architecture for the computer-aided diagnosis of tuberculosis (TB) on chest radiograph (CXR) images. A series of truncated EfficientNet-B0 models are proposed, systematically removing blocks to reduce model complexity while maintaining diagnostic accuracy. The B0(-3) model, which eliminates three blocks, emerges as a highly efficient confi...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
a06431de-3d84-5f98-9954-53457db99a6a
DOI
10.20944/preprints202605.0663.v1
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Truncating the EfficientNet-B0 for Computer Aided Diagnosis of TuberculosisDOI 10.20944/preprints202605.0663.v1
Assessing Diagnostic Accuracy and Viability of AI-Assisted Tuberculosis Detection in Northern Indian Healthcare Facilities: A Multicenter StudySemantic proximity
The Development and Evaluation of AI-based Tuberculosis Screening with a Digital Stethoscope used to Capture Lung Sounds. A Case-Control StudySemantic proximity
Artificial Intelligence for Tuberculosis Screening and Detection: From Evidence to Policy and ImplementationSemantic proximity
TB-Net: A Tailored, Self-Attention Deep Convolutional Neural Network Design For Detection of Tuberculosis Cases From Chest X-Ray ImagesSemantic proximity
Pretraining Diversity and Clinical Metric Optimization Achieve State-of-the-Art Performance on ChestX-ray14Semantic proximity
Comparative Analysis of Deep Learning Architectures for Multi-Disease Classification of Chest X-raysSemantic proximity
An Optimized EfficientNet-B3 Based Lung Cancer Detection Framework with Stratified Splitting and Deployment-Ready Torch Script IntegrationSemantic proximity
Development and Validation of a Deep Learning Model for Detecting Signs of Tuberculosis on Chest Radiographs among US-bound Immigrants and RefugeesSemantic proximity
A Lightweight Deep Learning Framework for Reliable Microscopy-Based Diagnosis of Cutaneous LeishmaniasisSemantic proximity
CT-Based Radiomics Analysis of Consolidation Characteristics in Differentiating Pulmonary Disease of Nontuberculous Mycobacterium from TuberculosisSemantic proximity
A Retrospective Multi-Source Clinical Validation of Lenek Intelligent Radiology Assistant: An Artificial Intelligence-Based Chest Radiograph Screening and Triage System for High-Burden Pulmonary and Cardiac Conditions in IndiaSemantic proximity
Explainability-Driven Tuberculosis Detection: GAN-Augmented CNN–Swin Transformer Hybrid Framework with Signal Processing InsightsSemantic proximity
Select a neighboring publication to make it the new centre.