ZooCNN: A Zero-Order Optimized Convolutional Neural Network for Pneumonia Classification Using Chest Radiographs

Journal of Imaging, Journal Year: 2025, Volume and Issue: 11(1), P. 22 - 22

Published: Jan. 13, 2025

Pneumonia, a leading cause of mortality in children under five, is usually diagnosed through chest X-ray (CXR) images due to its efficiency and cost-effectiveness. However, the shortage radiologists Least Developed Countries (LDCs) emphasizes need for automated pneumonia diagnostic systems. This article presents Deep Learning model, Zero-Order Optimized Convolutional Neural Network (ZooCNN), Optimization (Zoo)-based CNN model classifying CXR into three classes, Normal Lungs (NL), Bacterial Pneumonia (BP), Viral (VP); this utilizes Adaptive Synthetic Sampling (ADASYN) approach ensure class balance Kaggle Images (Pneumonia) dataset. Conventional models, though promising, face challenges such as overfitting have high computational costs. The use ZooPlatform (ZooPT), hyperparameter finetuning strategy, on baseline finetunes hyperparameters provides modified architecture, ZooCNN, with 72% reduction weights. was trained, tested, validated ZooCNN achieved an accuracy 97.27%, sensitivity 97.00%, specificity 98.60%, F1 score 97.03%. results were compared contemporary models highlight efficacy classification (PC), offering potential tool aid physicians clinical settings.

Language: Английский

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Enhanced Multi-Model Deep Learning for Rapid and Precise Diagnosis of Pulmonary Diseases Using Chest X-Ray Imaging

Diagnostics, Journal Year: 2025, Volume and Issue: 15(3), P. 248 - 248

Published: Jan. 22, 2025

Background: The global burden of respiratory diseases such as influenza, tuberculosis, and viral pneumonia necessitates rapid, accurate diagnostic tools to improve healthcare responses. Current methods, including RT-PCR chest radiography, face limitations in accuracy, speed, accessibility, cost-effectiveness, especially resource-constrained settings, often delaying treatment increasing transmission. Methods: This study introduces an Enhanced Multi-Model Deep Learning (EMDL) approach address these challenges. EMDL integrates ensemble five pre-trained deep learning models (VGG-16, VGG-19, ResNet, AlexNet, GoogleNet) with advanced image preprocessing (histogram equalization contrast enhancement) a novel multi-stage feature selection optimization pipeline (PCA, SelectKBest, Binary Particle Swarm Optimization (BPSO), Grey Wolf (BGWO)). Results: Evaluated on two independent X-ray datasets, achieved high accuracy the multiclass classification pneumonia, tuberculosis. combined enhancement strategies significantly improved precision model robustness. Conclusions: framework provides scalable efficient solution for accessible pulmonary disease diagnosis, potentially improving efficacy patient outcomes, particularly resource-limited settings.

Language: Английский

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Artificial intelligence and machine learning in critical care research

Journal of Critical Care, Journal Year: 2024, Volume and Issue: 82, P. 154791 - 154791

Published: March 25, 2024

Language: Английский

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Optimizing Lung Condition Categorization through a Deep Learning Approach to Chest X-ray Image Analysis

BioMedInformatics, Journal Year: 2024, Volume and Issue: 4(3), P. 2002 - 2021

Published: Sept. 10, 2024

Background: Evaluating chest X-rays is a complex and high-demand task due to the intrinsic challenges associated with diagnosing wide range of pulmonary conditions. Therefore, advanced methodologies are required categorize multiple conditions from X-ray images accurately. Methods: This study introduces an optimized deep learning approach designed for multi-label categorization images, covering broad spectrum conditions, including lung opacity, normative states, COVID-19, bacterial pneumonia, viral tuberculosis. An model based on modified VGG16 architecture SE blocks was developed applied large dataset images. The evaluated against state-of-the-art techniques using metrics such as accuracy, F1-score, precision, recall, area under curve (AUC). Results: VGG16-SE demonstrated superior performance across all metrics. achieved accuracy 98.49%, F1-score 98.23%, precision 98.41%, recall 98.07% AUC 98.86%. Conclusion: provides effective categorizing X-rays. model’s high various suggests its potential integration into clinical workflows, enhancing speed disease diagnosis.

Language: Английский

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Lung X-ray image segmentation algorithm based on Multihead Self-Attention Mechanism (MSAG) optimizing Unet networks

Applied and Computational Engineering, Journal Year: 2024, Volume and Issue: 67(1), P. 160 - 166

Published: July 16, 2024

In this paper, the Multihead Self-Attention Mechanism (MSAG) is used to optimize Unet network for accurate segmentation of lung X-ray images. By introducing MSAG module, ability capture global and local correlations enhanced, which effectively improves accuracy results. The introduction multi-head self-attention mechanism enables have more powerful modelling generalization capabilities, can process various types images stably efficiently. dataset divided into training, validation test sets according ratio 4:3:3. loss gradually converges during training process, model learns data features patterns, gap between them real labels reduced. performance on set good no over-fitting occurs, demonstrating generalize unseen data. evaluation metrics show an IoU 0.85, a Dice 0.92, Accuracy 0.88, proving that accurately extract segmentation. This study has achieved satisfactory results in field medical by optimizing structure new techniques, are positive significance improving efficiency image

Language: Английский

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Modeling and prediction of set‑up errors in breast cancer image‑guided radiotherapy using the Gaussian mixture model

Oncology Letters, Journal Year: 2024, Volume and Issue: 28(6)

Published: Sept. 30, 2024

The aim of the present study was to develop a prediction model for set-up error distribution in breast cancer image-guided radiotherapy (IGRT) using Gaussian mixture (GMM). To achieve this, errors data 80 patients with were selected, and GMM used model. predicted center points, covariance probability calculated compared planning target volume (PTV) margin formula. A total 1,200 sets IGRT collected. results parameters showed that mainly direction µ

Language: Английский

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