Enhancing Brain Tumor Segmentation Using Berkeley Wavelet Transformation and Improved SVM

The Open Bioinformatics Journal, Journal Year: 2025, Volume and Issue: 18(1)

Published: Feb. 19, 2025

Aims This research gives insight into the various machine learning models like enhanced Support Vector Machines (SVM), Convolutional Neural Networks (CNN), Recurrent (RNN), and Artificial (ANN) in brain tumor recognition by medical imaging. provides an accurate model for allowing a better form of diagnostic method neuro-oncology, with help precision, recall, F1-score metrics. The present study, therefore, also basis on which further predictive image analysis can be developed. Background study is premised critical need improved tools within imaging fight against prevalence tumors. A showing meaningful performance practices detection includes SVM, CNN, RNN, ANN. have been evaluated based their accuracy, F1 score to investigate potential. Consequently, addressing subject neuro-oncological diagnostics were evaluated. Objective seeks critically evaluate four different models: ANN, detecting tumor. It will determined from this has highest recall finding then lead improvement techniques neuro-oncology. Methods methodology involved detailed assessment Each was focused ability detect tumors data, examining models' identifying complex patterns varied feature spaces. Results outcome reveals that Machine (SVM) performed compared other models, demonstrating impressive 97.6% accuracy. In case it achieved 95.76% effectively hierarchical features. RNN showed good accuracy 92.3%, pretty adequate sequential data treatment. ANN high 88.77%. These findings describe differences strengths both possible applications detection. Conclusion conclusively established how much potential emerged improve capabilities Addressing perspective, SVM ranked first. Again, proof its importance as tool medicine. Based these findings, development neuro-oncology increase treatment outcomes. lays fundamental foundation betterment any made future.

Language: Английский

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Utilizing Multi-layer Perceptron for Esophageal Cancer Classification Through Machine Learning Methods

The Open Public Health Journal, Journal Year: 2024, Volume and Issue: 17(1)

Published: Oct. 7, 2024

Aims This research paper aims to check the effectiveness of a variety machine learning models in classifying esophageal cancer through MRI scans. The current study encompasses Convolutional Neural Network (CNN), K-Nearest Neighbor (KNN), Recurrent (RNN), and Visual Geometry Group 16 (VGG16), among others which are elaborated this paper. identify most accurate model facilitate increased, improved diagnostic accuracy revolutionize early detection methods for dreadful disease. ultimate goal is, therefore, improve clinical practice performance its results with advanced techniques medical diagnosis. Background Esophageal poses critical problem oncologists since pathology is quite complex, death rate exceptionally high. Proper essential effective treatment survival. positive, but conventional not sensitive have low specificity. Recent progress brings new possibility high sensitivity specificity explores potentiality different machine-learning scans complement constraints traditional diagnostics approach. Objective aimed at verifying whether CNN, KNN, RNN, VGG16, amongst other models, correctly from review establishing all these best all. It plays role developing mechanisms that increase patient outcome confidence setting. Methods applies approach comparative analysis by using four unique classify was made possible intensive training validation standardized set data. model’s assessed evaluation metrics, included accuracy, precision, recall, F1 score. Results In cancers scans, found VGG16 be an adequate model, 96.66%. CNN took second position, 94.5%, showing efficient spatial pattern recognition. KNN RNN also showed commendable performance, accuracies 91.44% 88.97%, respectively, portraying their strengths proximity-based handling sequential These findings underline potential add significant value processes diagnosis models. Conclusion concluded techniques, mainly had escalated precision imaging. great while displayed detection, followed RNN. Thus, opportunities introducing computational clinics, might transform strategies patient-centered outcomes oncology.

Language: Английский

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Enhanced LSTM for heart disease prediction in IoT-enabled smart healthcare systems

THE SCIENTIFIC TEMPER, Journal Year: 2024, Volume and Issue: 15(02), P. 2238 - 2247

Published: June 15, 2024

Cardiac patients require prompt and effective treatment to prevent heart attacks through accurate prediction of disease. The prognosis disease is complex requires advanced knowledge expertise. Healthcare systems are increasingly integrated with the internet things (IoT) collect data from sensors for diagnosing predicting diseases. Current methods employ machine learning (ML) these tasks, but they often fall short in creating an intelligent framework due difficulties handling high-dimensional data. A groundbreaking health system leverages IoT optimized long short-term memory (LSTM) algorithm, enhanced by red deer (RD) accurately diagnose cardiac issues. Continuous monitoring blood pressure electrocardiograms (ECG) conducted monitor devices smartwatches linked patients. gathered combined using a feature fusion approach, integrating electronic medical records (EMR) sensor extraction process. RD-LSTM model classifies conditions as either normal or abnormal, its performance benchmarked against other deep-learning (DL) models. showed better improvement accuracy over previous

Language: Английский

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Deep Learning and MRI Biomarkers for Precise Lung Cancer Cell Detection and Diagnosis

The Open Bioinformatics Journal, Journal Year: 2024, Volume and Issue: 17(1)

Published: Sept. 19, 2024

Aim This research work aimed to combine different AI methods create a modular diagnosis system for lung cancer, including Convolutional Neural Network (CNN), K-Nearest Neighbors (KNN), VGG16, and Recurrent (RNN) on MRI biomarkers. Models have then been evaluated compared in their effectiveness detecting using meticulously selected dataset containing 2045 images, with emphasis being put documenting the benefits of multimodal approach attacking complexities disease. Background Lung cancer remains most common cause death world, partly because challenges late stage presentation. Although Magnetic Resonance Imaging (MRI) has become critical modality identification staging too often, its is curtailed by interpretative variance among radiologists. Recent advances machine learning hold great promise augmenting analysis perhaps even increasing diagnostic accuracy start timely treatment. In this work, integration advanced models biomarkers solve these problems investigated. Objective The purpose present paper was assess integrating various machine-learning diagnostics, such as CNN, KNN, RNN. involved 2,045 performances were investigated comparing performance metrics determine best configuration interconnection while underpinning necessity accurate diagnoses and, consequently, better patient outcomes. Methods For study, we used 70% training 30% validation. We four photos: Systematic measures included study: accuracy, recall, precision, F1 score. confusion matrices study power every model comprehend pragmatic use real-world predictive capability. Results scores found be convolutional neural network terms tested, F1. rest models, RNN, performed decently but slightly lower than CNN. in-depth through thus established reliability revealing immense insight into capability identifying true positives minimizing false negatives enhancing detection. Conclusion findings obtained shown further support potential improve diagnosis. high sensitivity specificity KNN model, robustness results from VGG16 RNN pointed feasibility detection cancer. Our strong approach, which might impact future practice oncology treatment strategies outcomes medical imaging.

Language: Английский

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