Deep learning-assisted medical image compression challenges and opportunities: systematic review

Neural Computing and Applications, Год журнала: 2024, Номер 36(17), С. 10067 - 10108

Опубликована: Апрель 30, 2024

Язык: Английский

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AI in infectious disease diagnosis and vaccine development

Methods in microbiology, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

Язык: Английский

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MCU-Net: A multi-prior collaborative deep unfolding network with gates-controlled spatial attention for accelerated MRI reconstruction

Neurocomputing, Год журнала: 2025, Номер 633, С. 129771 - 129771

Опубликована: Фев. 26, 2025

Язык: Английский

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Deep learning models in classifying primary bone tumors and bone infections based on radiographs

npj Precision Oncology, Год журнала: 2025, Номер 9(1)

Опубликована: Март 13, 2025

Primary bone tumors (PBTs) present significant diagnostic challenges due to their heterogeneous nature and similarities with infections. This study aimed develop an ensemble deep learning framework that integrates multicenter radiographs extensive clinical features accurately differentiate between PBTs We compared the performance of model four imaging models based solely on utilizing EfficientNet B3, B4, Vision Transformer, Swin Transformers. The patients were split into external dataset (N = 423) internal [including training 1044), test 354), validation set 171)]. outperformed models, achieving areas under curve (AUCs) 0.948 0.963 sets, respectively, accuracies 0.881 0.895. Its surpassed junior mid-level radiologists was comparable senior (accuracy: 83.6%). These findings underscore potential in enhancing precision for infections (Research Registration Unique Identifying Number (UIN): researchregistry10483 details are available at https://www.researchregistry.com/register-now#home/registrationdetails/6693845995ba110026aeb754/ ).

Язык: Английский

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A Novel Deep Learning model for detection of Pneumonia and Covid-19 variants from Chest X-ray images

International Journal of Computational and Experimental Science and Engineering, Год журнала: 2025, Номер 11(2)

Опубликована: Апрель 13, 2025

Pneumonia is an example of a past pandemic and continues to be serious health concern. In the USA, more than one million people are admitted in hospital with pneumonia every year, leading about 500,000 deaths. Chest X-ray imaging effective widely utilised method for diagnosing essential both healthcare epidemiological studies. COVID-19, viral infection initiated Wuhan, China towards end 2019, quickly spread across globe. It caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) has influenced millions globally. Analyzing images regarded as fastest simplest methods discovery, available at minimal cost many places. CT scans, on other way, mere advanced technique that can identify small changes composition internal organs. This uses 3-D computer technology along X-rays detailed examination. While scans provide body compositions, traditional sometimes occlude, making it difficult see fine details. The proposed model outlines framework classifying COVID-19 variants predicting new ones. As per results, ResNet_Seg achieved F1 score 99.96%, which higher CNN models tested. performance these assessed using datasets from SARS MERS, resulting accurate predictions. Future work will focus validating statistical methods. A relative analysis deep learning models, including CNN, ResNet, Darknet, conducted, enhancements through novel segmentation algorithm hyperparameter fine-tuning. results offer insights into developing reliable diagnostic methodologies machine techniques.

Язык: Английский

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Boundary guided network with two-stage transfer learning for gastrointestinal polyps segmentation

Expert Systems with Applications, Год журнала: 2023, Номер 240, С. 122503 - 122503

Опубликована: Ноя. 8, 2023

Язык: Английский

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Classifying chest x-rays for COVID-19 through transfer learning: a systematic review

Multimedia Tools and Applications, Год журнала: 2024, Номер unknown

Опубликована: Апрель 2, 2024

Язык: Английский

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Concatenated CNN-Based Pneumonia Detection Using a Fuzzy-Enhanced Dataset

Sensors, Год журнала: 2024, Номер 24(20), С. 6750 - 6750

Опубликована: Окт. 21, 2024

Pneumonia is a form of acute respiratory infection affecting the lungs. Symptoms viral and bacterial pneumonia are similar. Rapid diagnosis disease difficult, since polymerase chain reaction-based methods, which have greatest reliability, provide results in few hours, while ensuring high requirements for compliance with analysis technology professionalism personnel. This study proposed Concatenated CNN model detection combined fuzzy logic-based image improvement method. The enhancement process based on new fuzzification refinement algorithm, significantly improved quality feature extraction CCNN model. Four datasets, original upgraded images utilizing entropy, standard deviation, histogram equalization, were utilized to train algorithm. CCNN's performance was demonstrated be by entropy-added dataset producing best results. suggested attained remarkable classification metrics, including 98.9% accuracy, 99.3% precision, 99.8% F1-score, 99.6% recall. Experimental comparisons showed that worked better than traditional resulting higher diagnostic precision. demonstrates how well deep learning models sophisticated techniques work together analyze medical images.

Язык: Английский

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Multi-Level Training and Testing of CNN Models in Diagnosing Multi-Center COVID-19 and Pneumonia X-ray Images

Applied Sciences, Год журнала: 2023, Номер 13(18), С. 10270 - 10270

Опубликована: Сен. 13, 2023

This study aimed to address three questions in AI-assisted COVID-19 diagnostic systems: (1) How does a CNN model trained on one dataset perform test datasets from disparate medical centers? (2) What accuracy gains can be achieved by enriching the training with new images? (3) learned features elucidate classification results, and how do they vary among different models? To achieve these aims, four models—AlexNet, ResNet-50, MobileNet, VGG-19—were five rounds incrementally adding images baseline set comprising 11,538 chest X-ray images. In each round, models were tested decreasing levels of image similarity. Notably, all showed performance drops when containing outlier or sourced other clinics. Round 1, 95.2~99.2% was for Level 1 testing (i.e., same clinic but apart only), 94.7~98.3% 2 an external similar). However, drastically decreased 3 rotation deformation), mean sensitivity plummeting 99% 36%. For 4 another clinic), 97% 86%, 67%. Rounds 3, 25% 50% improved average Level-3 15% 23% 56% 71% 83%). 5, increased Level-4 81% 92% 95%, respectively. Among models, ResNet-50 demonstrated most robust across five-round training/testing phases, while VGG-19 persistently underperformed. Heatmaps intermediate activation visual correlations pneumonia manifestations insufficient explicitly explain classification. heatmaps at shed light progression models’ learning behavior.

Язык: Английский

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Mobile Diagnosis of COVID-19 by Biogeography-based Optimization-guided CNN

Mobile Networks and Applications, Год журнала: 2024, Номер unknown

Опубликована: Март 4, 2024

Язык: Английский

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