Implemented classification techniques for osteoporosis using deep learning from the perspective of healthcare analytics DOI
Liu Lili

Technology and Health Care, Год журнала: 2024, Номер 32(3), С. 1947 - 1965

Опубликована: Фев. 23, 2024

BACKGROUND: Osteoporosis is a medical disorder that causes bone tissue to deteriorate and lose density, increasing the risk of fractures. Applying Neural Networks (NN) analyze imaging data detect presence or severity osteoporosis in patients known as classification using Deep Learning (DL) algorithms. DL algorithms can extract relevant information from images discover intricate patterns could indicate osteoporosis. OBJECTIVE: DCNN biases must be initialized carefully, much like their weights. Biases are incorrectly might affect network’s learning dynamics hinder model’s ability converge an ideal solution. In this research, Convolutional (DCNNs) used, which have several benefits over conventional ML techniques for image processing. METHOD: One key DCNNs automatically Feature Extraction (FE) raw data. time-consuming procedure During training phase DCNNs, network learns recognize characteristics straight The Squirrel Search Algorithm (SSA) makes use combination Local (LS) Random (RS) inspired by foraging habits squirrels. RESULTS: method made it possible efficiently explore search space find prospective values while promising areas refine improve solutions. Effectively recognizing optimum nearly optimal solutions depends on balancing exploration exploitation. weight optimized with help SSA, enhances performance classification. CONCLUSION: comparative analysis state-of-the-art shows proposed SSA-based highly accurate, 96.57% accuracy.

Язык: Английский

Integrating YOLO and 3D U-Net for COVID-19 Diagnosis on Chest CT Scans DOI
Jorge Valverde-Rebaza, Guilherme Rettore Andreis, Pedro Shiguihara-Juárez

и другие.

Опубликована: Июнь 26, 2024

Язык: Английский

Процитировано

1
MD-DCNN: Multi-Scale Dilation-Based Deep Convolution Neural Network for epilepsy detection using electroencephalogram signals DOI
Mohan Karnati, Geet Sahu,

Akanksha Yadav

и другие.

Knowledge-Based Systems, Год журнала: 2024, Номер 301, С. 112322 - 112322

Опубликована: Авг. 3, 2024

Язык: Английский

Процитировано

1
A cognitive few-shot learning for medical diagnosis: A case study on cleft lip and palate and Parkinson’s disease DOI
Pei Yin, Junjie Song, Yassine Bouteraa

и другие.

Expert Systems with Applications, Год журнала: 2024, Номер 262, С. 125713 - 125713

Опубликована: Ноя. 4, 2024

Язык: Английский

Процитировано

1
A systematic analysis and review of COVID-19 detection techniques using CT image DOI

JAMALUDEEN Ameera Beegom,

T. Brindha

Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization, Год журнала: 2023, Номер unknown, С. 1 - 14

Опубликована: Июнь 8, 2023

An unprecedented pandemic, named COVID-19, impacts the entire world and has been experienced in 2020. Due to lack of treatment, all researchers each every field concentrated deal with it. Primarily computer science, contribution involves development approaches for detection, diagnosis prediction COVID-19 scenarios. In this field, Deep Learning (DL) Data Science are most extensively exploited approaches. This review outlines 50 research papers also presents different identifying COVID-19. Here, these classified analysed into various categories survey details, like software tools employed, utilised datasets, published years performance metrics, those papers. Moreover, collected information is reviewed graphical regarding result analysis presented. The gaps problems raised conventional explained. For review, future work on basis issues identified from strategies. Additionally, exhibit that MATLAB tool used detection Convolutional Neural Network (CNN) model frequently approach detection.

Язык: Английский

Процитировано

1
Implemented classification techniques for osteoporosis using deep learning from the perspective of healthcare analytics DOI
Liu Lili

Technology and Health Care, Год журнала: 2024, Номер 32(3), С. 1947 - 1965

Опубликована: Фев. 23, 2024

BACKGROUND: Osteoporosis is a medical disorder that causes bone tissue to deteriorate and lose density, increasing the risk of fractures. Applying Neural Networks (NN) analyze imaging data detect presence or severity osteoporosis in patients known as classification using Deep Learning (DL) algorithms. DL algorithms can extract relevant information from images discover intricate patterns could indicate osteoporosis. OBJECTIVE: DCNN biases must be initialized carefully, much like their weights. Biases are incorrectly might affect network’s learning dynamics hinder model’s ability converge an ideal solution. In this research, Convolutional (DCNNs) used, which have several benefits over conventional ML techniques for image processing. METHOD: One key DCNNs automatically Feature Extraction (FE) raw data. time-consuming procedure During training phase DCNNs, network learns recognize characteristics straight The Squirrel Search Algorithm (SSA) makes use combination Local (LS) Random (RS) inspired by foraging habits squirrels. RESULTS: method made it possible efficiently explore search space find prospective values while promising areas refine improve solutions. Effectively recognizing optimum nearly optimal solutions depends on balancing exploration exploitation. weight optimized with help SSA, enhances performance classification. CONCLUSION: comparative analysis state-of-the-art shows proposed SSA-based highly accurate, 96.57% accuracy.

Язык: Английский

Процитировано

0