Deep Network-Based Comprehensive Parotid Gland Tumor Detection

Academic Radiology, Год журнала: 2023, Номер 31(1), С. 157 - 167

Опубликована: Июнь 3, 2023

Язык: Английский

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Impact of harmonization on the reproducibility of MRI radiomic features when using different scanners, acquisition parameters, and image pre-processing techniques: a phantom study

Medical & Biological Engineering & Computing, Год журнала: 2024, Номер 62(8), С. 2319 - 2332

Опубликована: Март 27, 2024

Abstract This study investigated the impact of ComBat harmonization on reproducibility radiomic features extracted from magnetic resonance images (MRI) acquired different scanners, using various data acquisition parameters and multiple image pre-processing techniques a dedicated MRI phantom. Four scanners were used to acquire an nonanatomic phantom as part TCIA RIDER database. In fast spin-echo inversion recovery (IR) sequences, several durations employed, including 50, 100, 250, 500, 750, 1000, 1500, 2000, 2500, 3000 ms. addition, 3D spoiled gradient recalled echo (FSPGR) sequence was investigate flip angles (FA): 2, 5, 10, 15, 20, 25, 30 degrees. Nineteen compartments manually segmented. Different approaches pre-process each image: Bin discretization, Wavelet filter, Laplacian Gaussian, logarithm, square, square root, gradient. Overall, 92 first-, second-, higher-order statistical extracted. also applied features. Finally, Intraclass Correlation Coefficient (ICC) Kruskal-Wallis’s (KW) tests implemented assess robustness The number non-significant in KW test ranged between 0–5 29–74 for 31–91 37–92 three times tests, 0–33 34–90 FAs, 3–68 65–89 IRs before after harmonization, with techniques, respectively. ICC over 90% 0–8 6–60 11–75 17–80 3–83 9–84 3–49 3–63 use IRs, FAs has great However, majority scanner-robust is robust IR FA. Among effective MR images, one scanner have negligible might affect large extent. significantly Graphical

Язык: Английский

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Flight traits of dengue-infected Aedes aegypti mosquitoes

Computers in Biology and Medicine, Год журнала: 2024, Номер 171, С. 108178 - 108178

Опубликована: Фев. 19, 2024

Understanding the flight behaviour of dengue-infected mosquitoes can play a vital role in various contexts, including modelling disease risks and developing effective interventions against dengue. Studies on locomotor activity have often faced challenges terms methodology. Some studies used small tubes, which impacted natural movement mosquitoes, while others that cages did not capture three-dimensional flights, despite naturally flying three dimensions. In this study, we utilised Mask RCNN (Region-based Convolutional Neural Network) along with cubic spline interpolation to comprehensively track Aedes aegypti mosquitoes. This analysis considered number parameters as characteristics mosquito flight, duration, Euclidean distance, speed, volume (space) covered during flights. The accuracy achieved for detection tracking was 98.34% 100% resting Notably, interpolated data accounted only 0.31%, underscoring reliability results. Flight traits results revealed exposure dengue virus significantly increases duration (p-value 0.0135 × 10−3) flights 0.029) whilst decreasing total compared uninfected study observe any evident impact distance 0.064) speed aegypti. These highlight intricate relationship between infection aegypti, providing valuable insights into transmission dynamics. focused mosquitoes; future research explore other arboviruses behaviour.

Язык: Английский

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Reinforced Collaborative-Competitive Representation for Biomedical Image Recognition

Interdisciplinary Sciences Computational Life Sciences, Год журнала: 2025, Номер unknown

Опубликована: Янв. 22, 2025

Язык: Английский

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Emotion detection from ECG signals with different learning algorithms and automated feature engineering

Signal Image and Video Processing, Год журнала: 2023, Номер 17(7), С. 3783 - 3791

Опубликована: Май 30, 2023

Язык: Английский

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An adaptive ensemble deep learning framework for reliable detection of pandemic patients

Computers in Biology and Medicine, Год журнала: 2023, Номер 168, С. 107836 - 107836

Опубликована: Дек. 7, 2023

Язык: Английский

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Classification of breast lesions in ultrasound images using deep convolutional neural networks: transfer learning versus automatic architecture design

Medical & Biological Engineering & Computing, Год журнала: 2023, Номер 62(1), С. 135 - 149

Опубликована: Сен. 22, 2023

Abstract Deep convolutional neural networks (DCNNs) have demonstrated promising performance in classifying breast lesions 2D ultrasound (US) images. Exiting approaches typically use pre-trained models based on architectures designed for natural images with transfer learning. Fewer attempts been made to design customized specifically this purpose. This paper presents a comprehensive evaluation learning solutions and automatically networks, analyzing the accuracy robustness of different recognition three folds. First, we develop six DCNN (BNet, GNet, SqNet, DsNet, RsNet, IncReNet) Second, adapt Bayesian optimization method optimize CNN network (BONet) lesions. A retrospective dataset 3034 US collected from various hospitals is then used evaluation. Extensive tests show that BONet outperforms other models, exhibiting higher (83.33%), lower generalization gap (1.85%), shorter training time (66 min), less model complexity (approximately 0.5 million weight parameters). We also compare diagnostic all against by experienced radiologists. Finally, explore saliency maps explain classification decisions models. Our investigation shows can assist comprehending decisions. Graphical

Язык: Английский

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Certainty weighted voting-based noise correction for crowdsourcing

Pattern Recognition, Год журнала: 2024, Номер 150, С. 110325 - 110325

Опубликована: Фев. 8, 2024

Язык: Английский

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Emotion Fusion-Sense (Emo Fu-Sense) – A novel multimodal emotion classification technique

Biomedical Signal Processing and Control, Год журнала: 2024, Номер 94, С. 106224 - 106224

Опубликована: Март 28, 2024

Язык: Английский

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iDDCS: Cervical intraepithelial neoplasia severity detection during vagina birth using artificial intelligence approach

Multidisciplinary Science Journal, Год журнала: 2025, Номер 7(8), С. 2025192 - 2025192

Опубликована: Фев. 2, 2025

To diagnose cervical cancer, this paper uses patient Pap tests to look for abnormal tissue growth in the cervix region of pregnant women. By undergoing routine identify any precancers and treating them, cancer can be avoided. The test scans cells at unusual or dysplasia-related alterations. However, traditional manual examination smears under microscope is vulnerable error. effectively categorize cells, a brand-new framework built on powerful features Support vector machines using convolutional neural network (SVM) models are proposed paper. performance algorithms was assessed based various evaluation metrics, including accuracy, sensitivity, specificity, false positive rate, negative predictive value, F score, error training time. Among three CNN tested, Faster R-CNN achieved an accuracy 93.7%, SSD reached 95.9%, YOLOv8 had highest 96.6% image detection. For SVM algorithm’s classification detection capabilities, average rates CIN1, CIN2, CIN3 were 90%, 89%, 81%, respectively, as per dataset. results suggested that CNN-SVM model with robust might used CIN CIN-C early stages. This novel method most effective among other unsupervised methods diagnosis. We recommend revolutionary approach combines NLP technology iDDCS Another goal research use extract relevant information from medical records, pathology reports, clinical statistics.

Язык: Английский

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