Towards ovarian cancer diagnostics: A vision transformer-based computer-aided diagnosis framework with enhanced interpretability DOI Creative Commons
Abdulrahman Alahmadi

Results in Engineering, Год журнала: 2024, Номер 23, С. 102651 - 102651

Опубликована: Авг. 2, 2024

Ovarian cancer, a significant threat to women's health, demands innovative diagnostic approaches. This paper introduces groundbreaking Computer-Aided Diagnosis (CAD) framework for the classification of ovarian integrating Vision Transformer (ViT) models and Local Interpretable Model-agnostic Explanations (LIME). ViT models, including ViT-Base-P16-224-In21K, ViT-Base-P16-224, ViT-Base-P32-384, ViT-Large-P32-384, exhibit exceptional accuracy, precision, recall, overall robust performance across diverse evaluation metrics. The incorporation stacked model further enhances performance. Experimental results, conducted on UBC-OCEAN training testing datasets, highlight proficiency in accurately classifying cancer subtypes based histopathological images. ViT-Large-P32-384 stands out as top performer, achieving 98.79% accuracy during 97.37% testing. Visualizations, Receiver Operating Characteristic (ROC) curves (LIME), provide insights into discriminative capabilities enhance interpretability. proposed CAD represents advancement diagnostics, offering promising avenue accurate transparent multi-class

Язык: Английский

New era of artificial intelligence and machine learning-based detection, diagnosis, and therapeutics in Parkinson’s disease DOI

Rohan Gupta,

Smita Kumari,

Anusha Senapati

и другие.

Ageing Research Reviews, Год журнала: 2023, Номер 90, С. 102013 - 102013

Опубликована: Июль 8, 2023

Язык: Английский

Процитировано

62
Artificial Intelligence-Based Voice Assessment of Patients with Parkinson’s Disease Off and On Treatment: Machine vs. Deep-Learning Comparison DOI Creative Commons
Giovanni Costantini, Valerio Cesarini, Pietro Leo

и другие.

Sensors, Год журнала: 2023, Номер 23(4), С. 2293 - 2293

Опубликована: Фев. 18, 2023

Parkinson’s Disease (PD) is one of the most common non-curable neurodegenerative diseases. Diagnosis achieved clinically on basis different symptoms with considerable delays from onset processes in central nervous system. In this study, we investigated early and full-blown PD patients based analysis their voice characteristics aid commonly employed machine learning (ML) techniques. A custom dataset was made hi-fi quality recordings vocal tasks gathered Italian healthy control subjects patients, divided into diagnosed, off-medication hand, mid-advanced treated L-Dopa other. Following current state-of-the-art, several ML pipelines were compared usingdifferent feature selection classification algorithms, deep also explored a CNN architecture. Results show how feature-based achieve comparable results terms classification, KNN, SVM naïve Bayes classifiers performing similarly, slight edge for KNN. Much more evident predominance CFS as best selector. The selected features act relevant biomarkers capable differentiating subjects, untreated patients.

Язык: Английский

Процитировано

51
A Comprehensive Survey on Aquila Optimizer DOI Open Access
Buddhadev Sasmal, Abdelazim G. Hussien, Arunita Das

и другие.

Archives of Computational Methods in Engineering, Год журнала: 2023, Номер 30(7), С. 4449 - 4476

Опубликована: Июнь 7, 2023

Язык: Английский

Процитировано

37
Comprehensive machine and deep learning analysis of sensor-based human activity recognition DOI
Hossam Magdy Balaha, Asmaa El-Sayed Hassan

Neural Computing and Applications, Год журнала: 2023, Номер 35(17), С. 12793 - 12831

Опубликована: Март 8, 2023

Язык: Английский

Процитировано

32
An AI-based novel system for predicting respiratory support in COVID-19 patients through CT imaging analysis DOI Creative Commons
Ibrahim Shawky Farahat, Ahmed Sharafeldeen, Mohammed Ghazal

и другие.

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

Опубликована: Янв. 8, 2024

Abstract The proposed AI-based diagnostic system aims to predict the respiratory support required for COVID-19 patients by analyzing correlation between lesions and level of provided patients. Computed tomography (CT) imaging will be used analyze three levels received patient: Level 0 (minimum support), 1 (non-invasive such as soft oxygen), 2 (invasive mechanical ventilation). begin segmenting from CT images creating an appearance model each lesion using a 2D, rotation-invariant, Markov–Gibbs random field (MGRF) model. Three MGRF-based models created, one support. This suggests that able differentiate different severity in decide patient neural network-based fusion system, which combines estimates Gibbs energy models. were assessed 307 COVID-19-infected patients, achieving accuracy $$97.72\%\pm 1.57$$ 97.72 % ± 1.57 , sensitivity $$97.76\%\pm 4.08$$ 97.76 4.08 specificity $$98.87\%\pm 2.09$$ 98.87 2.09 indicating high prediction accuracy.

Язык: Английский

Процитировано

16
A concentrated machine learning-based classification system for age-related macular degeneration (AMD) diagnosis using fundus images DOI Creative Commons

Aya A. Abd El-Khalek,

Hossam Magdy Balaha,

Norah Saleh Alghamdi

и другие.

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

Опубликована: Янв. 29, 2024

Abstract The increase in eye disorders among older individuals has raised concerns, necessitating early detection through regular examinations. Age-related macular degeneration (AMD), a prevalent condition over 45, is leading cause of vision impairment the elderly. This paper presents comprehensive computer-aided diagnosis (CAD) framework to categorize fundus images into geographic atrophy (GA), intermediate AMD, normal, and wet AMD categories. crucial for precise age-related enabling timely intervention personalized treatment strategies. We have developed novel system that extracts both local global appearance markers from images. These are obtained entire retina iso-regions aligned with optical disc. Applying weighted majority voting on best classifiers improves performance, resulting an accuracy 96.85%, sensitivity 93.72%, specificity 97.89%, precision 93.86%, F1 ROC 95.85%, balanced 95.81%, sum 95.38%. not only achieves high but also provides detailed assessment severity each retinal region. approach ensures final aligns physician’s understanding aiding them ongoing follow-up patients.

Язык: Английский

Процитировано

14
IHHO: an improved Harris Hawks optimization algorithm for solving engineering problems DOI Creative Commons

Dalia T. Akl,

Mahmoud M. Saafan,

Amira Y. Haikal

и другие.

Neural Computing and Applications, Год журнала: 2024, Номер 36(20), С. 12185 - 12298

Опубликована: Апрель 20, 2024

Abstract Harris Hawks optimization (HHO) algorithm was a powerful metaheuristic for solving complex problems. However, HHO could easily fall within the local minimum. In this paper, we proposed an improved (IHHO) different engineering tasks. The focused on random location-based habitats during exploration phase and strategies 1, 3, 4 exploitation phase. modified hawks in wild would change their perch strategy chasing pattern according to updates both phases. To avoid being stuck solution, values were generated using logarithms exponentials explore new regions more quickly locations. evaluate performance of algorithm, IHHO compared other five recent algorithms [grey wolf optimization, BAT teaching–learning-based moth-flame whale algorithm] as well three modifications (BHHO, LogHHO, MHHO). These optimizers had been applied benchmarks, namely standard CEC2017, CEC2019, CEC2020, 52 benchmark functions. Moreover, six classical real-world problems tested against prove efficiency algorithm. numerical results showed superiority algorithms, which proved visually convergence curves. Friedman's mean rank statistical test also inducted calculate algorithms. Friedman indicated that ranked first HHO.

Язык: Английский

Процитировано

12
A novel approach for Parkinson’s disease diagnosis using deep learning and Harris Hawks optimization algorithm with handwritten samples DOI
Siamak Hadadi,

Soodabeh Poorzaker Arabani

Multimedia Tools and Applications, Год журнала: 2024, Номер 83(34), С. 81491 - 81510

Опубликована: Март 9, 2024

Язык: Английский

Процитировано

9
ChiGa-Net: A genetically optimized neural network with refined deeply extracted features using χ2 statistical score for trustworthy Parkinson’s disease detection DOI

Liaqat Ali,

Man-Fai Leung, Muhammad Asghar Khan

и другие.

Neurocomputing, Год журнала: 2025, Номер unknown, С. 129450 - 129450

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

1
Transformative Approaches in Breast Cancer Detection: Integrating Transformers into Computer-Aided Diagnosis for Histopathological Classification DOI Creative Commons
Majed Alwateer, Amna Bamaqa, Mohammad Farsi

и другие.

Bioengineering, Год журнала: 2025, Номер 12(3), С. 212 - 212

Опубликована: Фев. 20, 2025

Breast cancer (BC) remains a leading cause of cancer-related mortality among women worldwide, necessitating advancements in diagnostic methodologies to improve early detection and treatment outcomes. This study proposes novel twin-stream approach for histopathological image classification, utilizing both histopathologically inherited vision-based features enhance precision. The first stream utilizes Virchow2, deep learning model designed extract high-level features, while the second employs Nomic, transformer model, capture spatial contextual information. fusion these streams ensures comprehensive feature representation, enabling achieve state-of-the-art performance on BACH dataset. Experimental results demonstrate superiority approach, with mean accuracy 98.60% specificity 99.07%, significantly outperforming single-stream methods related studies. Statistical analyses, including paired t-tests, ANOVA, correlation studies, confirm robustness reliability model. proposed not only improves but also offers scalable efficient solution clinical applications, addressing challenges resource constraints increasing demands.

Язык: Английский

Процитировано

1