Metaheuristic Based Feature Aggregator for Enhanced Classification of Microarray Data

IETE Journal of Research, Год журнала: 2025, Номер unknown, С. 1 - 11

Опубликована: Май 27, 2025

Язык: Английский

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Enhanced Hybrid Feature Extraction and Selection based on OCT Images for Diabetic Macular Edema Classification

Journal of Innovative Image Processing, Год журнала: 2025, Номер 7(2), С. 315 - 332

Опубликована: Июнь 1, 2025

In recent decades, Diabetic Macular Edema (DME) has emerged as a significant cause of vision loss among diabetic patients due to retinal fluid leakage. To address this challenge, reliable and efficient diagnostic methods are essential. The proposed methodology aims facilitate early detection through multi-stage process, including feature extraction, selection, classification.For we introduce the H2A2Net model, which incorporates Dense Spectral-Spatial Module (DSSM) that employs 3D convolutional DenseNet-inspired layers extract spectral-spatial features. This is complemented by Hybrid Resolution (HRM) designed achieve fine spatial detail multi-scale process. Additionally, Double Attention (DAM) implemented capture global cross-channel interactions, utilizing both pixel-wise channel-wise attenuation. Feature selection conducted using Cuckoo Search Spider Monkey Optimization (CSSMO), effectively processes local searches enable high-value classification phase, hybrid AdaBoost-Backpropagation Neural Network (BPNN) model employed, where BPNNs function weak classifiers whose outputs iteratively boosted create strong ensemble. Experimental results on CUHK dataset demonstrate method achieves an accuracy 97.4%, recall 97.6%, specificity 97%, F1-score 98%. These outcomes surpass those existing state-of-the-art methods, indicating approach offers enhanced robustness efficiency for DME classification.

Язык: Английский

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Metaheuristic integrated machine learning classification of colon cancer using STFT LASSO and EHO feature extraction from microarray gene expressions

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

Опубликована: Июль 17, 2024

Abstract The microarray gene expression data poses a tremendous challenge due to their curse of dimensionality problem. sheer volume features far surpasses available samples, leading overfitting and reduced classification accuracy. Thus the must be with efficient feature extraction methods reduce extract meaningful information enhance accuracy interpretability. In this research, we discover uniqueness applying STFT (Short Term Fourier Transform), LASSO (Least Absolute Shrinkage Selection Operator), EHO (Elephant Herding Optimisation) for extracting significant from lung cancer reducing database. is performed using following classifiers: Gaussian Mixture Model (GMM), Particle Swarm Optimization (PSO) GMM, Detrended Fluctuation Analysis (DFA), Naive Bayes classifier (NBC), Firefly Support Vector Machine Radial Basis Kernel (SVM-RBF) Flower Pollination (FPO) GMM. FPO-GMM attained highest in range 96.77, an F1 score 97.5, MCC 0.92 Kappa 0.92. reported results underline significance utilizing STFT, LASSO, data. These methodologies also help improved early diagnosis enhanced

Язык: Английский

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WGCNA and Machine Learning-based Integrative Bioinformatics Analysis for Identifying Key Genes of Colorectal Cancer

IEEE Access, Год журнала: 2024, Номер 12, С. 144350 - 144363

Опубликована: Янв. 1, 2024

Язык: Английский

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Evaluation of Hand Action Classification Performance Using Machine Learning Based on Signals from Two sEMG Electrodes

Sensors, Год журнала: 2024, Номер 24(8), С. 2383 - 2383

Опубликована: Апрель 9, 2024

Classification-based myoelectric control has attracted significant interest in recent years, leading to prosthetic hands with advanced functionality, such as multi-grip hands. Thus far, high classification accuracies have been achieved by increasing the number of surface electromyography (sEMG) electrodes or adding other sensing mechanisms. While many prescribed still adopt two-electrode sEMG systems, detailed studies on signal processing and performance are lacking. In this study, nine able-bodied participants were recruited perform six typical hand actions, from which signals two acquired using a Delsys Trigno Research+ acquisition system. Signal machine learning algorithms, specifically, linear discriminant analysis (LDA), k-nearest neighbors (KNN), support vector machines (SVM), used study accuracies. Overall accuracy 93 ± 2%, action-specific 97 F1-score 87 7% achieved, comparable those reported multi-electrode systems. The highest SVM algorithm compared LDA KNN algorithms. A logarithmic relationship between features was revealed, plateaued at five features. These comprehensive findings may potentially contribute strategies for commonly systems further improve functionality.

Язык: Английский

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Revolutionizing Colon Histopathology Glandular Segmentation Using an Ensemble Network With Watershed Algorithm

International Journal of Imaging Systems and Technology, Год журнала: 2024, Номер 34(5)

Опубликована: Сен. 1, 2024

ABSTRACT Colorectal adenocarcinoma, the most prevalent form of colon cancer, originates in glandular structures intestines, presenting histopathological abnormalities affected tissues. Accurate gland segmentation is crucial for identifying these potentially fatal abnormalities. While recent methodologies have shown success segmenting glands benign tissues, their efficacy diminishes when applied to malignant tissue segmentation. This study aims develop a robust learning algorithm using convolutional neural network (CNN) segment histology images. The methodology employs CNN based on U‐Net architecture, augmented by weighted ensemble that integrates DenseNet 169, Inception V3, and Efficientnet B3 as backbone models. Additionally, segmented boundaries are refined watershed algorithm. Evaluation Warwick‐QU dataset demonstrates promising results model, achieving an F1 score 0.928 0.913, object dice coefficient 0.923 0.911, Hausdorff distances 38.97 33.76 test sets A B, respectively. These compared with outcomes from GlaS challenge (MICCAI 2015) existing research findings. Furthermore, our model validated publicly available named LC25000, visual inspection reveals results, further validating approach. proposed underscores advantages amalgamating diverse models, highlighting potential techniques enhance tasks beyond individual capabilities.

Язык: Английский

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An explainable ensemble approach for advanced brain tumor classification applying Dual-GAN mechanism and feature extraction techniques over highly imbalanced data

PLoS ONE, Год журнала: 2024, Номер 19(9), С. e0310748 - e0310748

Опубликована: Сен. 27, 2024

Brain tumors are one of the leading diseases imposing a huge morbidity rate across world every year. Classifying brain accurately plays crucial role in clinical diagnosis and improves overall healthcare process. ML techniques have shown promise classifying based on medical imaging data such as MRI scans. These aid detecting planning treatment early, improving patient outcomes. However, image datasets frequently affected by significant class imbalance, especially when benign outnumber malignant number. This study presents an explainable ensemble-based pipeline for tumor classification that integrates Dual-GAN mechanism with feature extraction techniques, specifically designed highly imbalanced data. facilitates generation synthetic minority samples, addressing imbalance issue without compromising original quality Additionally, integration different methods capturing precise informative features. proposes novel deep ensemble (DeepEFE) framework surpasses other benchmark learning models accuracy 98.15%. focuses achieving high while prioritizing stable performance. By incorporating Grad-CAM, it enhances transparency interpretability research identifies most relevant contributing parts input images toward accurate outcomes enhancing reliability proposed pipeline. The significantly improved Precision, Sensitivity F1-Score demonstrate effectiveness handling accuracy. Furthermore, explainability process to establish reliable model classification, encouraging their adoption practice promoting trust decision-making processes.

Язык: Английский

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An Optimized Two Stage U-Net Approach for Segmentation of Pancreas and Pancreatic Tumor.

MethodsX, Год журнала: 2024, Номер 13, С. 102995 - 102995

Опубликована: Окт. 4, 2024

Язык: Английский

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Ant-based feature and instance selection for multiclass imbalanced data

IEEE Access, Год журнала: 2024, Номер 12, С. 133952 - 133968

Опубликована: Янв. 1, 2024

Язык: Английский

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An Optimized Two Stage U-Net Approach for Segmentation of Pancreas and Pancreatic Tumor

Опубликована: Янв. 1, 2024

Язык: Английский

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