April 18, 2024, 4:44 a.m. | Tanzina Taher Ifty, Saleh Ahmed Shafin, Shoeb Mohammad Shahriar, Tashfia Towhid

cs.CV updates on arXiv.org arxiv.org

arXiv:2404.11428v1 Announce Type: cross
Abstract: Lung diseases remain a critical global health concern, and it's crucial to have accurate and quick ways to diagnose them. This work focuses on classifying different lung diseases into five groups: viral pneumonia, bacterial pneumonia, COVID, tuberculosis, and normal lungs. Employing advanced deep learning techniques, we explore a diverse range of models including CNN, hybrid models, ensembles, transformers, and Big Transfer. The research encompasses comprehensive methodologies such as hyperparameter tuning, stratified k-fold cross-validation, and transfer …

abstract advanced arxiv classification covid cs.cv cs.lg deep learning disease diseases eess.iv five global global health health images normal ray them tuberculosis type viral work xai x-ray

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

MLOps Engineer - Hybrid Intelligence

@ Capgemini | Madrid, M, ES

Analista de Business Intelligence (Industry Insights)

@ NielsenIQ | Cotia, Brazil