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

Diagnostics, Год журнала: 2023, Номер 13(11), С. 1924 - 1924

Опубликована: Май 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%.

Язык: Английский

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

Biomedical Signal Processing and Control, Год журнала: 2023, Номер 87, С. 105418 - 105418

Опубликована: Сен. 24, 2023

Язык: Английский

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

Multimedia Tools and Applications, Год журнала: 2024, Номер 83(39), С. 86833 - 86868

Опубликована: Июль 3, 2024

Язык: Английский

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

Journal of Applied Biomedicine, Год журнала: 2024, Номер 44(3), С. 731 - 758

Опубликована: Июль 1, 2024

Язык: Английский

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

Diagnostics, Год журнала: 2023, Номер 13(15), С. 2538 - 2538

Опубликована: Июль 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%.

Язык: 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, Год журнала: 2023, Номер 13(10), С. 1753 - 1753

Опубликована: Май 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%

Язык: 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, Год журнала: 2025, Номер unknown, С. 163 - 176

Опубликована: Янв. 1, 2025

Язык: Английский

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

Опубликована: Фев. 20, 2025

Язык: Английский

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Early detection of Alzheimer’s disease progression stages using hybrid of CNN and transformer encoder models

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

Опубликована: Май 14, 2025

Alzheimer's disease (AD) is a neurodegenerative disorder that affects memory and cognitive functions. Manual diagnosis prone to human error, often leading misdiagnosis or delayed detection. MRI techniques help visualize the fine tissues of brain cells, indicating stage progression. Artificial intelligence analyze with high accuracy extract subtle features are difficult diagnose manually. In this study, modern methodology was designed combines power CNN models (ResNet101 GoogLeNet) local deep Vision Transformer (ViT) global find relationships between image spots. First, images Open Access Imaging Studies Series (OASIS) dataset were improved by two filters: adaptive median filter (AMF) Laplacian filter. The ResNet101 GoogLeNet modified suit feature extraction task reduce computational cost. ViT architecture cost while increasing number attention vertices further discover patches. enhanced fed into proposed ViT-CNN methodology. maps accuracy. Deep model. partitioned 32 using 16 GoogLeNet, both size 64 features. encoded recognize spatial arrangement patch preserve relationship patches, helping self-attention layers distinguish patches based on their positions. They transformer encoder, which consisted six blocks multiple focus different patterns regions simultaneously. Finally, MLP classification classify each one four classes. ResNet101-ViT hybrid outperformed GoogLeNet-ViT achieved 98.7% accuracy, 95.05% AUC, 96.45% precision, 99.68% sensitivity, 97.78% specificity.

Язык: Английский

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InceptionNeXt-Transformer: A novel multi-scale deep feature learning architecture for multimodal breast cancer diagnosis

Biomedical Signal Processing and Control, Год журнала: 2025, Номер 110, С. 108116 - 108116

Опубликована: Май 29, 2025

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