A new superfluity deep learning model for detecting knee osteoporosis and osteopenia in X-ray images

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 25, 2024

This study proposes a new deep-learning approach incorporating superfluity mechanism to categorize knee X-ray images into osteoporosis, osteopenia, and normal classes. The suggests the use of two distinct types blocks. rationale is that, unlike conventional serially stacked layer, concept involves concatenating multiple layers, enabling features flow branches rather than single branch. Two datasets have been utilized for training, validating, testing proposed model. We transfer learning with pre-trained models, AlexNet ResNet50, comparing results those indicate that performance namely was inferior Superfluity DL architecture. model demonstrated highest accuracy (85.42% dataset1 79.39% dataset2) among all models.

Language: Английский

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Improved sarcoidosis disease detection using deep learning and histogram of oriented gradients with quantum SVM

Deleted Journal, Journal Year: 2025, Volume and Issue: 7(3)

Published: Feb. 19, 2025

Language: Английский

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Intelligent detection and grading diagnosis of fresh rib fractures based on deep learning

BMC Medical Imaging, Journal Year: 2025, Volume and Issue: 25(1)

Published: March 24, 2025

Abstract Background Accurate detection and grading of fresh rib fractures are crucial for patient management but remain challenging due to the complexity structures on CT images. Methods Chest images from 383 patients with were retrospectively analyzed. The dataset was divided into a training set ( n = 306) an internal testing 77). An external 50 public RibFrac included. Fractures classified severe non-severe categories. A modified YOLO-based deep learning model developed grading. Performance compared thoracic surgeons using precision, recall, mAP50, F1 score. Results showed excellent performance in diagnosing fractures. For all types test set, score 0.963, 0.934, 0.972, 0.948, respectively. outperformed varying experience levels (all p < 0.01). Conclusion proposed can automatically detect grade accuracy comparable that physicians. This helps improve diagnostic accuracy, reduce physician workload, save medical resources, strengthen health care resource-limited areas. Clinical trial number Not applicable.

Language: Английский

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Attention LinkNet-152: a novel encoder-decoder based deep learning network for automated spine segmentation

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 16, 2025

Abstract Segmenting the spine from CT images is crucial for diagnosing and treating spine-related conditions but remains challenging due to spine’s complex anatomy imaging artifacts. This study introduces a novel encoder-decoder-based deep learning approach, named LinkNet-152, tailored automated segmentation. The model integrates modified EfficientNetB7 encoder with attention modules enhance feature extraction by focusing on regions of interest. decoder leverages LinkNet architecture, replacing ResNet34 deeper ResNet152 improve segmentation accuracy. Additionally, gradient sensitivity-based pruning applied optimize model’s complexity computational efficiency. Evaluated VerSe 2019 2020 datasets, proposed achieves superior performance, Dice coefficient 96.85% Jaccard index 95.37%, outperforming state-of-the-art methods. These results highlight effectiveness in addressing challenges its potential advance clinical applications.

Language: Английский

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Exploring Pyridinium-Based Inhibitors of Cholinesterases: A Review of Synthesis, Efficacy, and Structural Insights

European Journal of Medicinal Chemistry Reports, Journal Year: 2025, Volume and Issue: unknown, P. 100270 - 100270

Published: April 1, 2025

Language: Английский

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Recent Advancements in Neuroimaging‐Based Alzheimer's Disease Prediction Using Deep Learning Approaches in e‐Health: A Systematic Review

Health Science Reports, Journal Year: 2025, Volume and Issue: 8(5)

Published: May 1, 2025

ABSTRACT Purpose Alzheimer's disease (AD) is a severe neurological that significantly impairs brain function. Timely identification of AD essential for appropriate treatment and care. This comprehensive review intends to examine current developments in deep learning (DL) approaches with neuroimaging diagnosis, where popular imaging types, reviews well‐known online accessible data sets, describes different algorithms used DL the correct initial evaluation are presented. Significance Conventional diagnostic techniques, including medical evaluations cognitive assessments, usually not identify stages Alzheimer's. Neuroimaging methods, when integrated have demonstrated considerable potential enhancing diagnosis categorization AD. models received significant interest due their capability its early phases automatically, which reduces mortality rate cost Method An extensive literature search was performed leading scientific databases, concentrating on papers published from 2021 2025. Research leveraging techniques such as magnetic resonance (MRI), positron emission tomography, functional (fMRI), so forth. The complies Preferred Reporting Items Systematic Reviews Meta‐Analyses (PRISMA) guidelines. Results Current show CNN‐based especially those utilizing hybrid transfer frameworks, outperform conventional methods. employing combination multimodal has enhanced precision. Still, challenges method interpretability, heterogeneity, limited exist issues. Conclusion considerably improved accuracy reliability neuroimaging. Regardless issues accessibility adaptability, studies into interpretability fusion provide clinical application. Further research should concentrate standardized rigorous validation architectures, understandable AI methodologies enhance effectiveness methods prediction.

Language: Английский

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Deep Learning for Automatic Classification of Fruits and Vegetables: Evaluation from the Perspectives of Efficiency and Accuracy

Türkiye teknoloji ve uygulamalı bilimler dergisi., Journal Year: 2024, Volume and Issue: 5(2), P. 151 - 171

Published: Oct. 5, 2024

Within the agricultural domain, accurately categorizing freshness levels of fruits and vegetables holds immense significance, as this classification enables early detection spoilage allows for appropriate grouping products based on their intended export destinations. These processes necessitate a system capable meticulously classifying while minimizing labor expenditures. The current study concentrates developing an advanced model that can effectively categorize status each fruit vegetable 'good,' 'medium,' or 'spoiled.' To achieve objective, various artificial intelligence models, including CNN, AlexNet, ResNet50, GoogleNet, VGG16, EfficientB3, have been implemented, attaining remarkable success rates 99.75%, 97.97%, 96.71%, 99.49%, 98.75%, 99.81%, respectively.

Language: Английский

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Alzheimer's Disease Prediction Using InceptionResNet Integrating Deep Learning Models

Advances in bioinformatics and biomedical engineering book series, Journal Year: 2024, Volume and Issue: unknown, P. 415 - 432

Published: Nov. 1, 2024

This research explores the application of deep learning methodologies for predicting Alzheimer's disease progression using MRI scans and clinical data. The study leverages InceptionResNet architecture, known its effectiveness in image classification tasks, to analyze from a dataset.Patients diagnosed with disease. methodology involves preprocessing images enhance quality standardize dimensions, followed by training on [mention hardware setup] platform framework]. Performance evaluation metrics including accuracy (92%), precision (89%), recall (91%), F1-score (90%) demonstrate model's robustness early-stage detection. Comparative analysis baseline models reveals significant improvements, affirming efficacy identifying markers. Insights gained model contribute understanding dynamics, highlighting potential early diagnosis intervention.

Language: Английский

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ViTBayesianNet: An adaptive deep bayesian network-aided alzheimer disease detection framework with vision transformer-based residual densenet for feature extraction using MRI images

Network Computation in Neural Systems, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 41

Published: Dec. 11, 2024

One of the most familiar types disease is Alzheimer's (AD) and it mainly impacts people over age limit 60. AD causes irreversible brain damage in humans. It difficult to recognize various stages AD, hence advanced deep learning methods are suggested for recognizing its initial stages. In this experiment, an effective model-based detection approach introduced provide treatment patient. Initially, essential MRI collected from benchmark resources. After that, gathered MRIs provided as input feature extraction phase. Also, important features image extracted by Vision Transformer-based Residual DenseNet (ViT-ResDenseNet). Later, retrieved applied stage. phase, detected using Adaptive Deep Bayesian Network (Ada-DBN). Additionally, attributes Ada-DBN optimized with help Enhanced Golf Optimization Algorithm (EGOA). So, implemented model accomplishes relatively higher reliability than existing techniques. The numerical results framework obtained accuracy value 96.35 which greater 91.08, 91.95, 93.95 attained EfficientNet-B2, TF- CNN, ViT-GRU, respectively.

Language: Английский

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A Review of Datasets, Optimization Strategies, and Learning Algorithms for Analyzing Alzheimer’s Dementia Detection

Neuropsychiatric Disease and Treatment, Journal Year: 2024, Volume and Issue: Volume 20, P. 2203 - 2225

Published: Nov. 1, 2024

Alzheimer's Dementia (AD) is a progressive neurological disorder that affects memory and cognitive function, necessitating early detection for its effective management. This poses significant challenge to global public health. The accurate of dementia crucial several reasons. First, timely facilitates intervention planning treatment. Second, precise diagnostic methods are essential distinguishing from other disorders medical conditions may present with similar symptoms. Continuous analysis improvements in have contributed advancements research. It helps identify new biomarkers, refine existing tools, foster the development innovative technologies, ultimately leading more efficient approaches dementia. paper presents critical multimodal imaging datasets, learning algorithms, optimisation techniques utilised context detection. focus on understanding challenges employing diverse modalities, such as MRI (Magnetic Resonance Imaging), PET (Positron Emission Tomography), EEG (ElectroEncephaloGram). study evaluated various machine deep models, transfer techniques, generative adversarial networks multi-modality data In addition, examination encompassing algorithms hyperparameter tuning strategies processing analysing images presented this discern their influence model performance generalisation. Thorough enhancement fundamental addressing healthcare posed by dementia, facilitating interventions, improving accuracy, advancing research neurodegenerative diseases.

Language: Английский

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