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

Technology and Health Care, Journal Year: 2024, Volume and Issue: 32(3), P. 1947 - 1965

Published: Feb. 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.

Language: Английский

A hierarchical multi-leadership sine cosine algorithm to dissolving global optimization and data classification: The COVID-19 case study DOI
Mingyang Zhong, Jiahui Wen, Jingwei Ma

et al.

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 164, P. 107212 - 107212

Published: July 6, 2023

Language: Английский

Citations

28
Nature-Inspired Algorithms-Based Optimal Features Selection Strategy for COVID-19 Detection Using Medical Images DOI
Law Kumar Singh, Munish Khanna, Himanshu Monga

et al.

New Generation Computing, Journal Year: 2024, Volume and Issue: 42(4), P. 761 - 824

Published: May 10, 2024

Language: Английский

Citations

14
Robust Medical Diagnosis: A Novel Two-Phase Deep Learning Framework for Adversarial Proof Disease Detection in Radiology Images DOI
Sheikh Burhan Ul Haque, Aasim Zafar

Deleted Journal, Journal Year: 2024, Volume and Issue: 37(1), P. 308 - 338

Published: Jan. 10, 2024

In the realm of medical diagnostics, utilization deep learning techniques, notably in context radiology images, has emerged as a transformative force. The significance artificial intelligence (AI), specifically machine (ML) and (DL), lies their capacity to rapidly accurately diagnose diseases from images. This capability been particularly vital during COVID-19 pandemic, where rapid precise diagnosis played pivotal role managing spread virus. DL models, trained on vast datasets have showcased remarkable proficiency distinguishing between normal COVID-19-affected cases, offering ray hope amidst crisis. However, with any technological advancement, vulnerabilities emerge. Deep learning-based diagnostic although proficient, are not immune adversarial attacks. These attacks, characterized by carefully crafted perturbations input data, can potentially disrupt models' decision-making processes. context, such could dire consequences, leading misdiagnoses compromised patient care. To address this, we propose two-phase defense framework that combines advanced image filtering techniques. We use modified algorithm enhance model's resilience against examples training phase. During inference phase, apply JPEG compression mitigate cause misclassification. evaluate our approach three models based ResNet-50, VGG-16, Inception-V3. perform exceptionally classifying images (X-ray CT) lung regions into normal, pneumonia, pneumonia categories. then assess vulnerability these targeted attacks: fast gradient sign method (FGSM), projected descent (PGD), basic iterative (BIM). results show significant drop model performance after greatly improves resistance maintaining high accuracy examples. Importantly, ensures reliability diagnosing clean

Language: Английский

Citations

12
Improved Latin hypercube sampling initialization-based whale optimization algorithm for COVID-19 X-ray multi-threshold image segmentation DOI Creative Commons
Zhen Wang, Dong Zhao, Ali Asghar Heidari

et al.

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

Published: June 9, 2024

Image segmentation techniques play a vital role in aiding COVID-19 diagnosis. Multi-threshold image methods are favored for their computational simplicity and operational efficiency. Existing threshold selection multi-threshold segmentation, such as Kapur based on exhaustive enumeration, often hamper efficiency accuracy. The whale optimization algorithm (WOA) has shown promise addressing this challenge, but issues persist, including poor stability, low efficiency, accuracy segmentation. To tackle these issues, we introduce Latin hypercube sampling initialization-based multi-strategy enhanced WOA (CAGWOA). It incorporates COS initialization strategy (COSI), an adaptive global search approach (GS), all-dimensional neighborhood mechanism (ADN). COSI leverages probability density functions created from sampling, ensuring even solution space coverage to improve the stability of model. GS widens exploration scope combat stagnation during iterations ADN refines convergence around optimal individuals CAGWOA's performance is validated through experiments various benchmark function test sets. Furthermore, apply CAGWOA alongside similar model comparative lung X-ray images infected patients. results demonstrate superiority, better detail preservation, clear boundaries, adaptability across different levels.

Language: Английский

Citations

7
Hybrid Archimedes Sine Cosine optimization enabled Deep Learning for multilevel brain tumor classification using MRI images DOI

M. Geetha,

V Srinadh,

J. Janet

et al.

Biomedical Signal Processing and Control, Journal Year: 2023, Volume and Issue: 87, P. 105419 - 105419

Published: Sept. 20, 2023

Language: Английский

Citations

15
SUPER-COUGH: A Super Learner-based ensemble machine learning method for detecting disease on cough acoustic signals DOI
E. Topuz, Yasin Kaya

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 93, P. 106165 - 106165

Published: Feb. 28, 2024

Language: Английский

Citations

4
ResUNet + + : a comprehensive improved UNet + + framework for volumetric semantic segmentation of brain tumor MR images DOI
Amrita Kaur, Yadwinder Singh,

Basavraj Chinagundi

et al.

Evolving Systems, Journal Year: 2024, Volume and Issue: 15(4), P. 1567 - 1585

Published: April 13, 2024

Language: Английский

Citations

4
An effective U-net model for diagnosing Covid-19 infection DOI Creative Commons
Shirin Kordnoori, Malihe Sabeti, Hamidreza Mostafaei

et al.

Intelligence-Based Medicine, Journal Year: 2024, Volume and Issue: 10, P. 100156 - 100156

Published: Jan. 1, 2024

Coronavirus disease 2019 (COVID-19) has become a pandemic all over the world and spread rapidly. To distinguish between infected non-infected areas, there is critical need for segmentation methods that can identify areas from Chest Computed Tomography (CT) scans. In recent years, deep learning most widely used approach medical image segmentation, including identification of in CT We propose an encoder-decoder based on U-NET architecture segmenting MedSeg dataset, which contains lung study effect input dimensions model's output results, we gave images with 224 × 224, 256 256, 512 as inputs to model. The results showed achieved higher compared 512, dicecoef 81.36, accuracy 87.65, sensitivity 84.71, specificity 88.35. Additionally, proposed model highest number correct answers previous U-net methods. be applied effective screening tool help primary service staff better refer suspected patients specialists.

Language: Английский

Citations

4
HDTN: hybrid duo-transformer network for liver and hepatic tumor segmentation in CT images DOI

D. Mohanapriya,

T. Guna Sekar

Evolving Systems, Journal Year: 2025, Volume and Issue: 16(1)

Published: Feb. 1, 2025

Language: Английский

Citations

0
Threats to medical diagnosis systems: analyzing targeted adversarial attacks in deep learning-based COVID-19 diagnosis DOI
Sheikh Burhan Ul Haque, Aasim Zafar, Syaiful Haq

et al.

Soft Computing, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 17, 2025

Language: Английский

Citations

0