ArticleDiagnostics (Basel, Switzerland)2022
Pneumonia Classification from X-ray Images with Inception-V3 and Convolutional Neural Network.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Enhancing esophageal cancer detection using a deep learning framework and a novel spectrum-aided vision enhancer for virtual narrow band imaging.Scientific reports · 2026Article
- Metaheuristic-Optimized Convolutional Neural Network for Automated Diagnosis of Viral Pneumonia and Tuberculosis from Chest X-Rays.Diagnostics (Basel, Switzerland) · 2026Article
- Application of deep learning methods in the classification of normal and pneumonia lung images.Frontiers in medicine · 2026Article
- Impact of quantization on various CNN architectures for bone fracture detection.Frontiers in medical technology · 2026Article
- Article
- COVID-19 Pneumonia Diagnosis Using Medical Images: Deep Learning-Based Transfer Learning Approach.JMIRx med · 2025Article
- An Integrated Deep Learning Model with EfficientNet and ResNet for Accurate Multi-Class Skin Disease Classification.Diagnostics (Basel, Switzerland) · 2025Article
- Explainable hybrid transformer for multi-classification of lung disease using chest X-rays.Scientific reports · 2025Article
- Novel approach for Arabic fake news classification using embedding from large language features with CNN-LSTM ensemble model and explainable AI.Scientific reports · 2024Article
- Diagnosis model of early Pneumocystis jirovecii pneumonia based on convolutional neural network: a comparison with traditional PCR diagnostic method.BMC pulmonary medicine · 2024Article
- Computer-aided analysis of radiological images for cancer diagnosis: performance analysis on benchmark datasets, challenges, and directions.EJNMMI reports · 2024Review
- White blood cells classification using multi-fold pre-processing and optimized CNN model.Scientific reports · 2024Article
- A Deep Convolutional Neural Network for Pneumonia Detection in X-ray Images with Attention Ensemble.Diagnostics (Basel, Switzerland) · 2024Article
- ANFIS Fuzzy convolutional neural network model for leaf disease detection.Frontiers in plant science · 2024Article
- Transfer learning based approach for lung and colon cancer detection using local binary pattern features and explainable artificial intelligence (AI) techniques.PeerJ. Computer science · 2024Article
- Efficient-gastro: optimized EfficientNet model for the detection of gastrointestinal disorders using transfer learning and wireless capsule endoscopy images.PeerJ. Computer science · 2024Article
- Breast cancer detection employing stacked ensemble model with convolutional features.Cancer biomarkers : section A of Disease markers · 2024Article
- FacialNet: facial emotion recognition for mental health analysis using UNet segmentation with transfer learning model.Frontiers in computational neuroscience · 2024Article
- Study on the Common Molecular Mechanism of Metabolic Acidosis and Myocardial Damage Complicated by Neonatal Pneumonia.Metabolites · 2023Article
- Detection of Drowsiness among Drivers Using Novel Deep Convolutional Neural Network Model.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Pneumonia is one of the leading causes of death in both infants and elderly people, with approximately 4 million deaths each year. It may be a virus, bacterial, or fungal, depending on the contagious pathogen that damages the lung's tiny air sacs (alveoli). Patients with underlying disorders such as asthma, a weakened immune system, hospitalized babies, and older persons on ventilators are all at risk, particularly if pneumonia is not detected early. Despite the existing approaches for its diagnosis, low accuracy and efficiency require further research for more accurate systems. This study is a similar endeavor for the detection of pneumonia by the use of X-ray images. The dataset is preprocessed to make it suitable for transfer learning tasks. Different pre-trained convolutional neural network (CNN) variants are utilized, including VGG16, Inception-v3, and ResNet50. Ensembles are made by incorporating CNN with Inception-V3, VGG-16, and ResNet50. Besides the common evaluation metrics, the performance of the pre-trained and ensemble deep learning models is measured with Cohen's kappa as well as the area under the curve (AUC). Experimental results show that Inception-V3 with CNN attained the highest accuracy and recall score of 99.29% and 99.73%, respectively.
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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.