ViTBayesianNet: An adaptive deep bayesian network-aided alzheimer disease detection framework with vision transformer-based residual densenet for feature extraction using MRI images DOI
Rajasekar Mohan, Rajesh Arunachalam, Neha Verma

и другие.

Network Computation in Neural Systems, Год журнала: 2024, Номер unknown, С. 1 - 41

Опубликована: Дек. 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.

Язык: Английский

A new superfluity deep learning model for detecting knee osteoporosis and osteopenia in X-ray images DOI Creative Commons
Soaad M. Naguib, M. Saleh, Hanaa M. Hamza

и другие.

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Окт. 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.

Язык: Английский

Процитировано

5
Improved sarcoidosis disease detection using deep learning and histogram of oriented gradients with quantum SVM DOI Creative Commons
Aleka Melese Ayalew, Worku Abebe Degife, Nigus Wereta Asnake

и другие.

Deleted Journal, Год журнала: 2025, Номер 7(3)

Опубликована: Фев. 19, 2025

Язык: Английский

Процитировано

0
Intelligent detection and grading diagnosis of fresh rib fractures based on deep learning DOI Creative Commons
Tongxin Li, Mingyi Liao, Yong Fu

и другие.

BMC Medical Imaging, Год журнала: 2025, Номер 25(1)

Опубликована: Март 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.

Язык: Английский

Процитировано

0
Attention LinkNet-152: a novel encoder-decoder based deep learning network for automated spine segmentation DOI Creative Commons

Aqsa Dastgir,

Bin Wang, Muhammad Usman Saeed

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 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.

Язык: Английский

Процитировано

0
Exploring Pyridinium-Based Inhibitors of Cholinesterases: A Review of Synthesis, Efficacy, and Structural Insights DOI Creative Commons
Efraín Polo-Cuadrado,

Rojas-Peña Cristian,

A. Krogfelt Karen

и другие.

European Journal of Medicinal Chemistry Reports, Год журнала: 2025, Номер unknown, С. 100270 - 100270

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
Recent Advancements in Neuroimaging‐Based Alzheimer's Disease Prediction Using Deep Learning Approaches in e‐Health: A Systematic Review DOI Creative Commons
Zia‐ur‐Rehman, Mohd Khalid Awang, Ghulam Ali

и другие.

Health Science Reports, Год журнала: 2025, Номер 8(5)

Опубликована: Май 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.

Язык: Английский

Процитировано

0
Comparative Analysis of Convolutional Neural Network and Support Vector Machine for the Prediction of Alzheimer's Disease DOI

Nimish Selot,

Aayush Panwa,

Anju Shukla

и другие.

Communications in computer and information science, Год журнала: 2025, Номер unknown, С. 56 - 66

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Classification of the stages of Alzheimer’s disease based on three-dimensional lightweight neural networks DOI Creative Commons
Jun Li, Juntong Liu, Su Yang

и другие.

PeerJ Computer Science, Год журнала: 2025, Номер 11, С. e2897 - e2897

Опубликована: Май 15, 2025

Alzheimer’s disease is a neurodegenerative that seriously threatens the life and health of elderly. This study used three-dimensional lightweight neural networks to classify stages explore relationship between variations brain tissue. The CAT12 preprocess magnetic resonance images got three kinds preprocessed images: standardized images, segmented gray matter white images. were train four respectively, evaluation metrics are calculated. accuracies for classifying (cognitively normal, mild cognitive impairment, disease) in above 96%, precisions recalls 94%. found classification cognitively best results can be obtained by training with impairment disease, analyzed process normal more obvious at beginning, while not obvious. As progresses, tend become significant, both significant development disease.

Язык: Английский

Процитировано

0
KNN algorithm for accurate identification of IFP lesions in the knee joint: a multimodal MRI study DOI Creative Commons
Peng Zhou, Zhenyan Liu,

Jiang Dai

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Май 25, 2025

Knee-related disorders represent a major global health concern and are leading cause of pain mobility impairment, particularly in older adults. In clinical medicine, the precise identification classification knee joint diseases essential for early diagnosis effective treatment. This study presents novel approach identifying infrapatellar fat pad (IFP) lesions using K-Nearest Neighbor (KNN) algorithm combination with multimodal Magnetic Resonance Imaging (MRI) techniques, specifically mDxion-Quant (mDQ) T2 mapping (T2m). These imaging methods provide quantitative parameters such as fraction (FF), T2*, values. A set derived features was constructed through feature engineering to better capture variations within IFP. were used train KNN model classifying conditions. The proposed method achieved accuracies 94.736% 92.857% on training testing datasets, respectively, outperforming CNN-Class8 benchmark. technique holds substantial potential detection pathologies, monitoring disease progression, evaluating post-surgical outcomes.

Язык: Английский

Процитировано

0
Deep Learning for Automatic Classification of Fruits and Vegetables: Evaluation from the Perspectives of Efficiency and Accuracy DOI
Demet Parlak Sönmez, Şafak Kılıç

Türkiye teknoloji ve uygulamalı bilimler dergisi., Год журнала: 2024, Номер 5(2), С. 151 - 171

Опубликована: Окт. 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.

Язык: Английский

Процитировано

0