Automatic and Early Detection of Parkinson’s Disease by Analyzing Acoustic Signals Using Classification Algorithms Based on Recursive Feature Elimination Method

Diagnostics, Journal Year: 2023, Volume and Issue: 13(11), P. 1924 - 1924

Published: May 31, 2023

Parkinson's disease (PD) is a neurodegenerative condition generated by the dysfunction of brain cells and their 60-80% inability to produce dopamine, an organic chemical responsible for controlling person's movement. This causes PD symptoms appear. Diagnosis involves many physical psychological tests specialist examinations patient's nervous system, which several issues. The methodology method early diagnosis based on analysing voice disorders. extracts set features from recording voice. Then machine-learning (ML) methods are used analyse diagnose recorded distinguish cases healthy ones. paper proposes novel techniques optimize evaluating selected hyperparameter tuning ML algorithms diagnosing dataset was balanced synthetic minority oversampling technique (SMOTE) were arranged according contribution target characteristic recursive feature elimination (RFE) algorithm. We applied two algorithms, t-distributed stochastic neighbour embedding (t-SNE) principal component analysis (PCA), reduce dimensions dataset. Both t-SNE PCA finally fed resulting into classifiers support-vector machine (SVM), K-nearest neighbours (KNN), decision tree (DT), random forest (RF), multilayer perception (MLP). Experimental results proved that proposed superior existing studies in RF with algorithm yielded accuracy 97%, precision 96.50%, recall 94%, F1-score 95%. In addition, MLP 98%, 97.66%, 96%, 96.66%.

Language: Английский

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A self-learning deep neural network for classification of breast histopathological images

Biomedical Signal Processing and Control, Journal Year: 2023, Volume and Issue: 87, P. 105418 - 105418

Published: Sept. 24, 2023

Language: Английский

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Vision transformer based convolutional neural network for breast cancer histopathological images classification

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: 83(39), P. 86833 - 86868

Published: July 3, 2024

Language: Английский

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Analysis of WSI Images by Hybrid Systems with Fusion Features for Early Diagnosis of Cervical Cancer

Diagnostics, Journal Year: 2023, Volume and Issue: 13(15), P. 2538 - 2538

Published: July 31, 2023

Cervical cancer is one of the most common types malignant tumors in women. In addition, it causes death latter stages. Squamous cell carcinoma and aggressive form cervical must be diagnosed early before progresses to a dangerous stage. Liquid-based cytology (LBC) swabs are best commonly used for screening converted from glass slides whole-slide images (WSIs) computer-assisted analysis. Manual diagnosis by microscopes limited prone manual errors, tracking all cells difficult. Therefore, development computational techniques important as diagnosing many samples can done automatically, quickly, efficiently, which beneficial medical laboratories professionals. This study aims develop automated WSI image analysis models squamous dataset. Several systems have been designed analyze accurately distinguish progression. For proposed systems, were optimized show contrast edges low-contrast cells. Then, analyzed segmented isolated rest using Active Contour Algorithm (ACA). hybrid method between deep learning (ResNet50, VGG19 GoogLeNet), Random Forest (RF), Support Vector Machine (SVM) algorithms based on ACA algorithm. Another RF SVM fused features deep-learning (DL) (ResNet50-VGG19, VGG19-GoogLeNet, ResNet50-GoogLeNet). It concluded systems' performance that DL models' combined help significantly improve networks. The novelty this research combines extracted ResNet50-GoogLeNet) with images. results demonstrate SVM. network ResNet50-VGG19 achieved an AUC 98.75%, sensitivity 97.4%, accuracy 99%, precision 99.6%, specificity 99.2%.

Language: Английский

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Multimodal breast cancer hybrid explainable computer-aided diagnosis using medical mammograms and ultrasound Images

Journal of Applied Biomedicine, Journal Year: 2024, Volume and Issue: 44(3), P. 731 - 758

Published: July 1, 2024

Language: Английский

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Growth optimization-based stacked bidirectional long short-term recurrent neural network model for detecting breast cancer in Internet of Things healthcare environment

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 163 - 176

Published: Jan. 1, 2025

Language: Английский

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Growth Optimization–Based SBLRNN Model for Estimate Breast Cancer in IoT Healthcare Environment

Published: Feb. 20, 2025

Language: Английский

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Analyzing Histological Images Using Hybrid Techniques for Early Detection of Multi-Class Breast Cancer Based on Fusion Features of CNN and Handcrafted

Diagnostics, Journal Year: 2023, Volume and Issue: 13(10), P. 1753 - 1753

Published: May 17, 2023

Breast cancer is the second most common type of among women, and it can threaten women's lives if not diagnosed early. There are many methods for detecting breast cancer, but they cannot distinguish between benign malignant tumors. Therefore, a biopsy taken from patient's abnormal tissue an effective way to challenges facing pathologists experts in diagnosing including addition some medical fluids various colors, direction sample, small number doctors their differing opinions. Thus, artificial intelligence techniques solve these help clinicians resolve diagnostic differences. In this study, three techniques, each with systems, were developed diagnose multi binary classes datasets types 40× 400× factors. The first technique dataset using neural network (ANN) selected features VGG-19 ResNet-18. by ANN combined ResNet-18 before after principal component analysis (PCA). third analyzing hybrid features. handcrafted; handcrafted. handcrafted mixed extracted Fuzzy color histogram (FCH), local pattern (LBP), discrete wavelet transform (DWT) gray level co-occurrence matrix (GLCM) methods. With data set, reached precision 95.86%, accuracy 97.3%, sensitivity 96.75%, AUC 99.37%, specificity 99.81% images at magnification factor 400×. Whereas 99.74%, 99.7%, 100%, 99.85%, 100%

Language: Английский

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Vision transformer-convolution for breast cancer classification using mammography images: A comparative study

International Journal of Hybrid Intelligent Systems, Journal Year: 2024, Volume and Issue: 20(2), P. 67 - 83

Published: May 24, 2024

Breast cancer is a significant global health concern, highlighting the critical importance of early detection for effective treatment women’s health. While convolutional networks (CNNs) have been best analysing medical images, recent interest has emerged in leveraging vision transformers (ViTs) data analysis. This study aimed to conduct comprehensive comparison three systems self-attention transformer (VIT), compact convolution (CCT), and tokenlearner (TVIT) binary classification mammography images into benign cancerous tissue. Thorough experiments were performed using DDSM dataset, which consists 5970 7158 malignant images. The performance accuracy proposed models was evaluated, yielding results 99.81% VIT, 99.92% CCT, 99.05% TVIT. Additionally, compared these with current state-of-the-art metrics. findings demonstrate how convolution-attention mechanisms can effectively contribute development robust computer-aided diagnosing breast cancer. Notably, approach achieves high-performance while also minimizing computational resources required reducing decision time.

Language: Английский

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Machine Learning for Early Breast Cancer Detection

Journal of Engineering and Science in Medical Diagnostics and Therapy, Journal Year: 2024, Volume and Issue: 8(1)

Published: July 26, 2024

Abstract Globally, breast cancer (BC) remains a significant cause to female mortality. Early detection of BC plays an important role in reducing premature deaths. Various imaging techniques including ultrasound, mammogram, magnetic resonance imaging, histopathology, thermography, positron emission tomography, and microwave have been employed for obtaining images (BIs). This review provides comprehensive information different modalities publicly accessible BI sources. The advanced machine learning (ML) offer promising avenue replace human involvement detecting cancerous cells from BIs. article outlines various ML algorithms (MLAs) which extensively used identifying BIs at the early stages, categorizing them based on presence or absence malignancy. Additionally, addresses current challenges associated with application MLAs identification proposes potential solutions.

Language: Английский

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