Advancing artificial intelligence in healthcare: Diagnosing lung disease from chest X-rays using the VGG16 model

Advances in Artificial Intelligence (AI) have transformed many aspects of the healthcare industry, and the healthcare industry has been the primary beneficiary of this change. The study, titled Advances in Artificial Intelligence in Healthcare: Detecting Lung Disease from Chest X-Rays Using the PreTrained VGG16 Model explores the integration of AI in healthcare diagnostics, demonstrating its potential for business innovation and robust healthcare. Pneumonia remains a major global health problem that requires early diagnosis to reduce mortality and improve patient outcomes. While traditional diagnostic procedures are effective, they are time-consuming, labor-intensive, and laborious for electrical engineers, and pose challenges in narrow spaces. (CNN) Develop a cognitive search engine for lung diseases on the ImageNet dataset. Learning transitions were used to tune binary classification models to distinguish between positive and negative lung lesions on chest x-rays. Preprocessing steps such as image resizing, normalization, and data augmentation increase the generalizability and robustness of the model. The model is optimized using the Adam optimizer and categorical cross-entropy loss function for robustness and learning efficiency. ROC curve analysis. The results showed high classification accuracy and low false positive rates, which are important in clinical diagnosis. The model suggests continuous improvement during training while working towards improving accuracy. Unlike existing AI models that use complex visual techniques, this work provides insight into the decision-making processes of deep learning models by specifically introducing the elimination process. Business and economic analysis capabilities are driven by intelligence. Automated medical diagnostic machines reduce the reliance on specialists, increase hospital efficiency, reduce healthcare costs, and make it easier for people to access services. Good healthcare, especially in regions with limited medical resources. In addition, AI solutions have opened up new avenues for healthcare startups, telemedicine services, and cloud-based diagnostic platforms, spurring business innovation and scalability in the healthcare sector. The changing role of intelligence in developing accurate and effective medical diagnosis, as well as its impact on business, technology, and new treatments, are being explored. As AI continues to develop, its integration with healthcare is expected to revolutionize healthcare, providing significant solutions, explanations, and insights for disease diagnosis and management.

Authors

Publication Details

Journal
International Journal of Advanced Academic Studies
Published
2025-03-01
DOI
https://doi.org/10.33545/27068919.2025.v7.i3b.1568
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Advancing artificial intelligence in healthcare: Diagnosing lung disease from chest X-rays using the VGG16 model

Himanshu Bairwa, R. A. Jangid
International Journal of Advanced Academic Studies
Radiomics and Machine Learning in Medical Imaging
article

Advancing artificial intelligence in healthcare: Diagnosing lung disease from chest X-rays using the VGG16 model

Himanshu Bairwa, R. A. Jangid
article en

Abstract

Advances in Artificial Intelligence (AI) have transformed many aspects of the healthcare industry, and the healthcare industry has been the primary beneficiary of this change. The study, titled Advances in Artificial Intelligence in Healthcare: Detecting Lung Disease from Chest X-Rays Using the PreTrained VGG16 Model explores the integration of AI in healthcare diagnostics, demonstrating its potential for business innovation and robust healthcare. Pneumonia remains a major global health problem that requires early diagnosis to reduce mortality and improve patient outcomes. While traditional diagnostic procedures are effective, they are time-consuming, labor-intensive, and laborious for electrical engineers, and pose challenges in narrow spaces. (CNN) Develop a cognitive search engine for lung diseases on the ImageNet dataset. Learning transitions were used to tune binary classification models to distinguish between positive and negative lung lesions on chest x-rays. Preprocessing steps such as image resizing, normalization, and data augmentation increase the generalizability and robustness of the model. The model is optimized using the Adam optimizer and categorical cross-entropy loss function for robustness and learning efficiency. ROC curve analysis. The results showed high classification accuracy and low false positive rates, which are important in clinical diagnosis. The model suggests continuous improvement during training while working towards improving accuracy. Unlike existing AI models that use complex visual techniques, this work provides insight into the decision-making processes of deep learning models by specifically introducing the elimination process. Business and economic analysis capabilities are driven by intelligence. Automated medical diagnostic machines reduce the reliance on specialists, increase hospital efficiency, reduce healthcare costs, and make it easier for people to access services. Good healthcare, especially in regions with limited medical resources. In addition, AI solutions have opened up new avenues for healthcare startups, telemedicine services, and cloud-based diagnostic platforms, spurring business innovation and scalability in the healthcare sector. The changing role of intelligence in developing accurate and effective medical diagnosis, as well as its impact on business, technology, and new treatments, are being explored. As AI continues to develop, its integration with healthcare is expected to revolutionize healthcare, providing significant solutions, explanations, and insights for disease diagnosis and management.

International Journal of Advanced Academic StudiesVol. 7(3S)
Good health and well-being
Openalex Percentile: Top 74%
Radiomics and Machine Learning in Medical Imaging
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.