A Deep Learning based model for the Detection of Pneumonia from Chest X-Ray Images using VGG-16 and Neural Networks

Procedia Computer Science, Journal Year: 2023, Volume and Issue: 218, P. 357 - 366

Published: Jan. 1, 2023

Pneumonia is a viral infection which affects significant proportion of individuals, especially in developing and penurious countries where contamination, overcrowded, unsanitary living conditions are widespread, along with the lack healthcare infrastructures. produces pericardial effusion, disease wherein fluids fill chest create inhaling problems. It difficult step to recognize presence pneumonia quickly order receive treatment services improve survival chances. Deep learning, field artificial intelligence used successful development prediction models. There various ways detecting such as CT-scan, pulse oximetry, many more among most common way X-ray tomography. On other hand, examining X-rays (CXR) tough process susceptible subjective variability. In this work, deep learning(DL) model using VGG16 utilized for classifying two CXR image datasets. The Neural Networks (NN) provides an accuracy value 92.15%, recall 0.9308, precision 0.9428, F1-Score0.937 first dataset. Furthermore, experiment NN has been performed on another dataset containing 6,436 images pneumonia, normal covid-19. results second provide accuracy, recall, precision, F1-score 95.4%, 0.954, respectively. research outcome exhibits that better performance than Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), Naïve Bayes (NB) both Further, proposed work exhibit improved datasets 1 2 comparison existing

Language: Английский

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Artificial intelligence in clinical medicine: catalyzing a sustainable global healthcare paradigm

Frontiers in Artificial Intelligence, Journal Year: 2023, Volume and Issue: 6

Published: Aug. 29, 2023

As the demand for quality healthcare increases, systems worldwide are grappling with time constraints and excessive workloads, which can compromise of patient care. Artificial intelligence (AI) has emerged as a powerful tool in clinical medicine, revolutionizing various aspects care medical research. The integration AI medicine not only improved diagnostic accuracy treatment outcomes, but also contributed to more efficient delivery, reduced costs, facilitated better experiences. This review article provides an extensive overview applications history taking, examination, imaging, therapeutics, prognosis Furthermore, it highlights critical role played transforming developing nations.

Language: Английский

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A hybrid explainable ensemble transformer encoder for pneumonia identification from chest X-ray images

Journal of Advanced Research, Journal Year: 2022, Volume and Issue: 48, P. 191 - 211

Published: Sept. 7, 2022

Pneumonia is a microorganism infection that causes chronic inflammation of the human lung cells. Chest X-ray imaging most well-known screening approach used for detecting pneumonia in early stages. While chest-Xray images are mostly blurry with low illumination, strong feature extraction required promising identification performance. A new hybrid explainable deep learning framework proposed accurate disease using chest images. The workflow developed by fusing capabilities both ensemble convolutional networks and Transformer Encoder mechanism. backbone to extract features from raw input two different scenarios: (i.e., DenseNet201, VGG16, GoogleNet) B InceptionResNetV2, Xception). Whereas, built based on self-attention mechanism multilayer perceptron (MLP) identification. visual saliency maps derived emphasize crucial predicted regions end-to-end training process models over all scenarios performed binary multi-class classification scenarios. model recorded 99.21% performance terms overall accuracy F1-score task, while it achieved 98.19% 97.29% multi-classification task. For scenario, 97.22% 97.14% F1-score, 96.44% F1-score. multiclass 97.2% 95.8% 96.4% 94.9% could provide encouraging comparing individual, models, or even latest AI literature. code available here: https://github.com/chiagoziemchima/Pneumonia_Identificaton.

Language: Английский

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Pneumonia Classification from X-ray Images with Inception-V3 and Convolutional Neural Network

Diagnostics, Journal Year: 2022, Volume and Issue: 12(5), P. 1280 - 1280

Published: May 21, 2022

Pneumonia is one of the leading causes death in both infants and elderly people, with approximately 4 million deaths each year. It may be a virus, bacterial, or fungal, depending on contagious pathogen that damages lung's tiny air sacs (alveoli). Patients underlying disorders such as asthma, weakened immune system, hospitalized babies, older persons ventilators are all at risk, particularly if pneumonia not detected early. Despite existing approaches for its diagnosis, low accuracy efficiency require further research more accurate systems. This study similar endeavor detection by use X-ray images. The dataset preprocessed to make it suitable transfer learning tasks. Different pre-trained convolutional neural network (CNN) variants utilized, including VGG16, Inception-v3, ResNet50. Ensembles made incorporating CNN Inception-V3, VGG-16, Besides common evaluation metrics, performance ensemble deep models measured Cohen's kappa well area under curve (AUC). Experimental results show Inception-V3 attained highest recall score 99.29% 99.73%, respectively.

Language: Английский

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Deep learning model for fully automated breast cancer detection system from thermograms

PLoS ONE, Journal Year: 2022, Volume and Issue: 17(1), P. e0262349 - e0262349

Published: Jan. 14, 2022

Breast cancer is one of the most common diseases among women worldwide. It considered leading causes death women. Therefore, early detection necessary to save lives. Thermography imaging an effective diagnostic technique which used for breast with help infrared technology. In this paper, we propose a fully automatic system. First, U-Net network automatically extract and isolate area from rest body behaves as noise during model. Second, two-class deep learning model, trained scratch classification normal abnormal tissues thermal images. Also, it more characteristics dataset that helpful in training improve efficiency process. The proposed system evaluated using real data (A benchmark, database (DMR-IR)) achieved accuracy = 99.33%, sensitivity 100% specificity 98.67%. expected be tool physicians clinical use.

Language: Английский

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AltWOA: Altruistic Whale Optimization Algorithm for feature selection on microarray datasets

Computers in Biology and Medicine, Journal Year: 2022, Volume and Issue: 144, P. 105349 - 105349

Published: March 10, 2022

Language: Английский

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A systematic literature review on deep learning approaches for pneumonia detection using chest X-ray images

Multimedia Tools and Applications, Journal Year: 2023, Volume and Issue: 83(8), P. 24101 - 24151

Published: Aug. 9, 2023

Language: Английский

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A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using Radiography

Diagnostics, Journal Year: 2023, Volume and Issue: 13(1), P. 159 - 159

Published: Jan. 3, 2023

Chest X-ray radiography (CXR) is among the most frequently used medical imaging modalities. It has a preeminent value in detection of multiple life-threatening diseases. Radiologists can visually inspect CXR images for presence Most thoracic diseases have very similar patterns, which makes diagnosis prone to human error and leads misdiagnosis. Computer-aided (CAD) lung popular topics research. Machine learning (ML) deep (DL) provided techniques make this task more efficient faster. Numerous experiments various proved potential these techniques. In comparison previous reviews our study describes detail several publicly available datasets different presents an overview recent models using detect chest such as VGG, ResNet, DenseNet, Inception, EfficientNet, RetinaNet, ensemble methods that combine models. summarizes image preprocessing (enhancement, segmentation, bone suppression, data-augmentation) improve quality address data imbalance issues, well use DL speed-up process. This review also discusses challenges present published literature highlights importance interpretability explainability better understand models' detections. addition, it outlines direction researchers help develop effective early automatic

Language: Английский

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A Convolutional Neural Network ensemble model for Pneumonia Detection using chest X-ray images

Healthcare Analytics, Journal Year: 2023, Volume and Issue: 3, P. 100176 - 100176

Published: April 12, 2023

Pneumonia is a respiratory infection caused by microbes and other environmental factors. It infects the lungs causing buildup of fluid difficulty in breathing leading cause for death children under age 5 years. Timely detection proves essential preventing adverse consequences including death. However, most areas underdeveloped developing nations do not have access to conventional diagnostic measures, preventive measures adequate expert treatment. Computer-aided systems based on machine learning techniques can aid this task. smart may drawback requiring extensive hardware heavy computation power. The objective experiment develop lightweight, deployable accurate model Pneumonia. A Convolutional Neural Network architecture utilizing three different models varying kernel sizes was developed. outputs these were combined using novel weighted ensemble approach which proposes an adjustable threshold value change model's capabilities as required. flexible provides means adjust weightage given each output hence classification result depending actual case hand. evaluated metrics accuracy, recall, precision f1-score able achieve high recall 99.23% with 88.56% are critically values domain resulting almost no chances positive being misclassified. absence transfer or deep neural networks makes lightweight hence, plausibly diagnostic-aid solution. Further studies carried out find methods such – larger dataset, better preprocessing more improve performance.

Language: Английский

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A Systematic Review on Federated Learning in Medical Image Analysis

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 28628 - 28644

Published: Jan. 1, 2023

Federated Learning (FL) obtained a lot of attention to the academic and industrial stakeholders from beginning its invention. The eye-catching feature FL is handling data in decentralized manner which creates privacy preserving environment Artificial Intelligence (AI) applications. As we know medical includes marginal private information patients demands excessive protection disclosure unexpected destinations. In this paper, performed Systematic Literature Review (SLR) published research articles on based image analysis. Firstly, have collected different databases followed by PRISMA guidelines, then synthesized selected articles, finally provided comprehensive overview topic. order do that extracted core associated with implementation imaging articles. our findings briefly presented characteristics federated models, performance achieved models exclusively results comparison traditional ML models. addition, discussed open issues challenges implementing mentioned recommendations for future direction particular field. We believe SLR has successfully summarized state-of-the-art methods analysis using deep learning.

Language: Английский

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