Evaluating AI and Machine Learning Models in Breast Cancer Detection: A Review of Convolutional Neural Networks (CNN) and Global Research Trends

LatIA, Journal Year: 2024, Volume and Issue: 3, P. 117 - 117

Published: Oct. 18, 2024

Numerous studies have highlighted the significance of artificial intelligence (AI) in breast cancer diagnosis. However, systematic reviews AI applications this field often lack cohesion, with each study adopting a unique approach. The aim is to provide detailed examination AI's role diagnosis through citation analysis, helping categorize key areas that attract academic attention. It also includes thematic analysis identify specific research topics within category. A total 30,200 related and AI, published between 2015 2024, were sourced from databases such as IEEE, Scopus, PubMed, Springer, Google Scholar. After applying inclusion exclusion criteria, 32 relevant identified. Most these utilized classification models for prediction, high accuracy being most commonly reported performance metric. Convolutional Neural Networks (CNN) emerged preferred model many studies. findings indicate both quantity quality AI-based algorithms are increases given years. increasingly seen complement healthcare sector clinical expertise, target enhancing accessibility affordability worldwide.

Language: Английский

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Harnessing Machine Learning for Quantifying Vesicoureteral Reflux: A Promising Approach for Objective Assessment

International Journal of Online and Biomedical Engineering (iJOE), Journal Year: 2024, Volume and Issue: 20(11), P. 123 - 145

Published: Aug. 8, 2024

In this study, we evaluated the performance of various machine-learning models on multiple datasets labeled GR1, GR2, GR3, GR4, and GR5. We assessed using a range evaluation metrics, including AUC, CA, F1, precision, recall, MCC, specificity, log loss. The examined were logistic regression, decision tree, kNN, random forest, gradient boosting, neural network, AdaBoost, stochastic descent. results indicate that all consistently demonstrated outstanding across datasets, with most achieving perfect scores in metrics. exhibited high accuracy effectiveness accurately classifying instances. Although forests displayed slightly lower some theyi still maintained an overall level accuracy. findings highlight models’ ability to effectively learn underlying patterns within data make accurate predictions. low loss values further confirmed precise estimation probabilities. Consequently, these possess strong potential for practical applications domains, offering reliable robust classification capabilities.

Language: Английский

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Application of Artificial Intelligence for Diagnosing Tumors in the Female Reproductive System: A Systematic Review

Multidisciplinar, Journal Year: 2024, Volume and Issue: 3, P. 54 - 54

Published: Oct. 18, 2024

The diagnosis of tumors in the female reproductive system is crucial for effective treatment and patient outcomes. advent artificial intelligence (AI) has introduced new possibilities enhancing diagnostic accuracy efficiency. A comprehensive search across PubMed, Scopus, Web Science articles published from 2018 to 2023 on (AI), machine learning (ML), deep (DL), convolutional neural networks (CNN) diagnosing cancers yielded 15,900 articles. After a rigorous screening process excluding conference proceedings, book chapters, reports, non-English publications, duplicates, 98 unique peer-reviewed journal remained. These were further assessed relevance quality, resulting final inclusion 29 high-quality review includes summary various AI methodologies used, their accuracy, comparative performance against traditional methods. findings indicate significant improvement precision efficiency when employed. holds substantial promise system. Future research should focus larger-scale studies integration into clinical workflows fully realize its potential

Language: Английский

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Advancing Medical Image Analysis: The Role of Adaptive Optimization Techniques in Enhancing COVID-19 Detection, Lung Infection, and Tumor Segmentation

LatIA, Journal Year: 2024, Volume and Issue: 2, P. 74 - 74

Published: Sept. 29, 2024

Artificial intelligence (AI) holds significant potential to revolutionize healthcare by improving clinical practices and patient outcomes. This research explores the integration of AI in healthcare, focusing on methodologies such as machine learning, natural language processing, computer vision, which enable extraction valuable insights from complex medical imaging data. Through a comprehensive literature review, study highlights AI’s practical applications diagnostics, treatment planning, predicting Additionally, ethical issues, data privacy, legal frameworks are examined, emphasizing importance responsible usage healthcare. The findings demonstrate ability enhance diagnostic accuracy, streamline administrative tasks, optimize resource allocation, leading personalized treatments more efficient management. However, challenges remain, including quality, algorithm transparency, concerns, must be addressed ensure safe effective deployment. Continued research, collaboration between professionals experts, development robust regulatory essential for maximizing benefits while minimizing risks. underscores transformative stresses need multidisciplinary approach address complexities involved its widespread adoption

Language: Английский

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Diabetes Prediction and Management Using Machine Learning Approaches

Data & Metadata, Journal Year: 2025, Volume and Issue: 4, P. 545 - 545

Published: Feb. 18, 2025

Diabetes has emerged as a significant global health issue, especially with the increasing number of cases in many countries. This trend Underlines need for greater emphasis on early detection and proactive management to avert or mitigate severe complications this disease. Over recent years, machine learning algorithms have shown promising potential predicting diabetes risk are beneficial practitioners. Objective: study highlights prediction capabilities statistical non-statistical methods over classification 768 samples from Pima Indians Database. It consists demographic clinical features age, body mass index (BMI) blood glucose levels that greatly depend vulnerability against Diabetes. The experimentation assesses various types terms accuracy effectiveness regarding prediction. These include Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Naive Bayes, Support Vector Machine, Gradient Boosting Neural Network Models. results show algorithm gained highest predictive 78.57%, then Forest had second position 76.30% accuracy. findings techniques not just highly effective. Still, they also can potentially act screening tools within data-driven fashion valuable information who is more likely get affected. In addition, help realize timely intervention longer term, which step towards reducing outcomes disease burden attributable healthcare systems.

Language: Английский

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Improving Oral Cancer Outcomes Through Machine Learning and Dimensionality Reduction

Data & Metadata, Journal Year: 2025, Volume and Issue: 3

Published: Jan. 2, 2025

Oral cancer presents a formidable challenge in oncology, necessitating early diagnosis and accurate prognosis to enhance patient survival rates. Recent advancements machine learning data mining have revolutionized traditional diagnostic methodologies, providing sophisticated automated tools for differentiating between benign malignant oral lesions. This study comprehensive review of cutting-edge including Neural Networks, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), ensemble techniques, specifically applied the cancer. Through rigorous comparative analysis, our findings reveal that Networks surpass other models, achieving an impressive classification accuracy 93.6% predicting Furthermore, we underscore potential benefits integrating feature selection dimensionality reduction techniques model performance. These insights significant promise advanced bolstering detection, optimizing treatment strategies, ultimately improving outcomes realm oncology

Language: Английский

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AI in the Sky: Developing Real-Time UAV Recognition Systems to Enhance Military Security

Data & Metadata, Journal Year: 2024, Volume and Issue: 3

Published: Sept. 29, 2024

In an era where Unmanned Aerial Vehicles (UAVs) have become crucial in military surveillance and operations, the need for real-time accurate UAV recognition is increasingly critical. The widespread use of UAVs presents various security threats, requiring systems that can differentiate between benign objects, such as birds. This study conducts a comparative analysis advanced machine learning models to address challenge aerial classification diverse environmental conditions without system redesign. Large datasets were used train validate models, including Neural Networks, Support Vector Machines, ensemble methods, Random Forest Gradient Boosting Machines. These evaluated based on accuracy computational efficiency, key factors application. results indicate Networks provide best performance, demonstrating high distinguishing from findings emphasize significant potential enhance operational improve allocation defense resources. Overall, this research highlights effectiveness advocates integration into strengthen decision-making operations. Regular updates these are recommended keep pace with advancements technology, more agile stealthier designs

Language: Английский

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Automated Quantification of Vesicoureteral Reflux using Machine Learning with Advancing Diagnostic Precision

Data & Metadata, Journal Year: 2024, Volume and Issue: 4, P. 460 - 460

Published: Nov. 7, 2024

This article uses machine learning to quantify vesicoureteral reflux (VUR). VCUGs in pediatric urology are used diagnose VUR. The goal is increase diagnostic precision. Various models categorize VUR grades (Grade 1 Grade 5) and evaluated using performance metrics confusion matrices. Study datasets come from internet repositories with repository names accession numbers. Machine performed well across several measures. KNN, Random Forest, AdaBoost, CN2 Rule Induction consistently scored 100% AUC, CA, F1-score, precision, recall, MCC, specificity. These classified individually collectively. In contrast, the Constant model poorly all criteria, suggesting its inability reliably. With most excellent average ratings, excelled at grade categorization. Confusion matrices demonstrate that predict grades. large diagonal numbers of show regularly predicted effectively. However, model's constant 5 forecast reduced differentiation. study shows methods automate measurement. findings aid objective grading radiographic evaluation. accurately classifies learning-based techniques may clinical decision-making, patient outcomes.

Language: Английский

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Comparative Analysis of Advanced Data Mining Methods for Enhancing Medical Diagnosis and Prognosis

Data & Metadata, Journal Year: 2024, Volume and Issue: 3

Published: Oct. 29, 2024

Accurate and early diagnosis, coupled with precise prognosis, is critical for improving patient outcomes in various medical conditions. This paper focuses on leveraging advanced data mining techniques to address two key challenges: diagnosis prognosis. Diagnosis involves differentiating between benign malignant conditions, while prognosis aims predict the likelihood of recurrence after treatment. Despite significant advances imaging clinical collection, achieving high accuracy both remains a challenge. study provides comprehensive review state-of-the-art machine learning used including Neural Networks, K-Nearest Neighbors (KNN), Naïve Bayes, Logistic Regression, Decision Trees, Support Vector Machines (SVM). These methods are evaluated their ability process large, complex datasets produce actionable insights practitioners.We conducted thorough comparative analysis based performance metrics such as accuracy, Area Under Curve (AUC), precision, recall, specificity. Our findings reveal that Networks consistently outperform other terms diagnostic predictive capacity, demonstrating robustness handling high-dimensional nonlinear data. research underscores potential algorithms revolutionizing effective thus facilitating more personalized treatment plans improved healthcare outcomes.

Language: Английский

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AI Rx: Revolutionizing Healthcare Through Intelligence, Innovation, and Ethics

Deleted Journal, Journal Year: 2024, Volume and Issue: 4, P. 35 - 35

Published: Oct. 18, 2024

The integration of artificial intelligence (AI) in healthcare presents significant promise to enhance clinical procedures and patient outcomes. This research examines the setting, methodology, conclusions, issues associated with AI healthcare. swift proliferation digital health data, encompassing medical imaging records, has generated substantial prospects for applications. Artificial methodologies, including machine learning, natural language processing, computer vision, facilitate derivation insights from intricate datasets, hence improving decision-making. A thorough literature review practical applications AI, its roles diagnostics, treatment planning, outcome prediction. report also ethical issues, data protection, legal frameworks, which are crucial responsible application results illustrate AI's capacity diagnostic precision, administrative efficiency, optimise resource distribution, resulting tailored therapies improved administration. Nonetheless, obstacles persist, such as integrity, algorithm transparency, considerations, must be resolved guarantee secure efficient deployment AI. Continuous research, cooperation between experts, establishment comprehensive regulatory frameworks essential optimising advantages while minimising hazards. highlights transform healthcare, stressing necessity a multidisciplinary strategy effectively harness benefits tackle dilemmas.

Language: Английский

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