Deep Learning Models for Pneumonia Identification and Classification Based on X-Ray Images

Naralasetti, Veeranjaneyulu; Shaik, Reshmi Khadherbhi; Katepalli, Gayatri; Bodapati, Jyostna Devi

doi:10.18280/ts.380337

“…Researchers have utilized several methodologies, such as the pre-trained model approach, ensemble model approach, and from-scratch model approach, as shown in the Table. Naralasetti et al [ 74 ] used Deep CNN architecture and achieved a 91% accuracy rate. Ensemble models allow for a deeper understanding of the task and better results.…”

Section: Discussionmentioning

confidence: 99%

Automated Lung-Related Pneumonia and COVID-19 Detection Based on Novel Feature Extraction Framework and Vision Transformer Approaches Using Chest X-ray Images

Ukwuoma

¹

,

Qin

²

,

Heyat

³

et al. 2022

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According to research, classifiers and detectors are less accurate when images are blurry, have low contrast, or have other flaws which raise questions about the machine learning model’s ability to recognize items effectively. The chest X-ray image has proven to be the preferred image modality for medical imaging as it contains more information about a patient. Its interpretation is quite difficult, nevertheless. The goal of this research is to construct a reliable deep-learning model capable of producing high classification accuracy on chest x-ray images for lung diseases. To enable a thorough study of the chest X-ray image, the suggested framework first derived richer features using an ensemble technique, then a global second-order pooling is applied to further derive higher global features of the images. Furthermore, the images are then separated into patches and position embedding before analyzing the patches individually via a vision transformer approach. The proposed model yielded 96.01% sensitivity, 96.20% precision, and 98.00% accuracy for the COVID-19 Radiography Dataset while achieving 97.84% accuracy, 96.76% sensitivity and 96.80% precision, for the Covid-ChestX-ray-15k dataset. The experimental findings reveal that the presented models outperform traditional deep learning models and other state-of-the-art approaches provided in the literature.

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“…Examining deep learning models for pneumonia identification and classification based on X-ray images, the study contributes to the expanding literature on automated disease detection in radiological imaging. While the findings demonstrate potential applications in pneumonia diagnosis, addressing challenges such as data imbalance and model interpretability through additional research is essential to enhance the reliability and effectiveness of the proposed approach [11].…”

Section: Evaluation Of Deep Learning Modelsmentioning

confidence: 96%

Deep Learning Algorithm for Real-time Disease Detection and Classification in Radiological Images with Enhanced Diagnostic Accuracy

Nirmala Nithya Raju

2024

jes

0

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Radiological imaging plays a pivotal role in disease diagnosis, but the manual interpretation is prone to errors and time-consuming. This research explores the utilization of deep learning algorithms for real-time disease detection and classification in radiological images, aiming to enhance diagnostic accuracy and efficiency in healthcare settings. Deep learning algorithms offer a promising solution by automating the detection and classification process, potentially reducing diagnosis time and improving patient outcomes. However, deploying deep learning algorithms for real-time disease detection in radiological images presents several challenges. These challenges also consist of the need for diverse and large datasets for model training, addressing class imbalance and data variability, ensuring robustness to noise and artifacts, and interpreting model decisions for clinical validation. To overcome these challenges, this research proposes several methods, including data augmentation techniques for increasing dataset diversity, transfer learning from pre-trained models for leveraging existing knowledge, ensemble learning for combining multiple models for improved performance, and attention mechanisms to focus on relevant image regions. Additionally, techniques for uncertainty estimation and model interpretability are explored to enhance trust and acceptance of automated diagnosis systems in clinical practice. By addressing these challenges and implementing appropriate methods, deep learning algorithms show promise for real-time disease detection and classification in radiological images, offering a transformative approach to medical imaging analysis. Results show an accuracy of 97%, sensitivity and specificity at 97% and 95%, and an F1 score of 96%.

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“…Examining deep learning models for pneumonia identification and classification based on X-ray images, the study contributes to the expanding literature on automated disease detection in radiological imaging. While the findings demonstrate potential applications in pneumonia diagnosis, addressing challenges such as data imbalance and model interpretability through additional research is essential to enhance the reliability and effectiveness of the proposed approach [11].…”

Section: Evaluation Of Deep Learning Modelsmentioning

confidence: 96%

Deep Learning Algorithm for Real-time Disease Detection and Classification in Radiological Images with Enhanced Diagnostic Accuracy

Nirmala Nithya Raju

2024

jes

0

View full text Add to dashboard Cite

Radiological imaging plays a pivotal role in disease diagnosis, but the manual interpretation is prone to errors and time-consuming. This research explores the utilization of deep learning algorithms for real-time disease detection and classification in radiological images, aiming to enhance diagnostic accuracy and efficiency in healthcare settings. Deep learning algorithms offer a promising solution by automating the detection and classification process, potentially reducing diagnosis time and improving patient outcomes. However, deploying deep learning algorithms for real-time disease detection in radiological images presents several challenges. These challenges also consist of the need for diverse and large datasets for model training, addressing class imbalance and data variability, ensuring robustness to noise and artifacts, and interpreting model decisions for clinical validation. To overcome these challenges, this research proposes several methods, including data augmentation techniques for increasing dataset diversity, transfer learning from pre-trained models for leveraging existing knowledge, ensemble learning for combining multiple models for improved performance, and attention mechanisms to focus on relevant image regions. Additionally, techniques for uncertainty estimation and model interpretability are explored to enhance trust and acceptance of automated diagnosis systems in clinical practice. By addressing these challenges and implementing appropriate methods, deep learning algorithms show promise for real-time disease detection and classification in radiological images, offering a transformative approach to medical imaging analysis. Results show an accuracy of 97%, sensitivity and specificity at 97% and 95%, and an F1 score of 96%.

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Deep Learning Models for Pneumonia Identification and Classification Based on X-Ray Images

Cited by 13 publications

References 20 publications

Automated Lung-Related Pneumonia and COVID-19 Detection Based on Novel Feature Extraction Framework and Vision Transformer Approaches Using Chest X-ray Images

Automated Lung-Related Pneumonia and COVID-19 Detection Based on Novel Feature Extraction Framework and Vision Transformer Approaches Using Chest X-ray Images

Deep Learning Algorithm for Real-time Disease Detection and Classification in Radiological Images with Enhanced Diagnostic Accuracy

Deep Learning Algorithm for Real-time Disease Detection and Classification in Radiological Images with Enhanced Diagnostic Accuracy

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