Healthcare analytics (New York, N.Y.)最新文献_第3页

An analytical framework for enhancing brain signal classification through hybrid filtering and dimensionality reduction 一种通过混合滤波和降维增强脑信号分类的分析框架

Healthcare analytics (New York, N.Y.) Pub Date : 2025-12-01 Epub Date: 2025-11-09 DOI: 10.1016/j.health.2025.100435

Rajani Rai B , Karunakara Rai B , Mamatha A S , Nikshitha

{"title":"An analytical framework for enhancing brain signal classification through hybrid filtering and dimensionality reduction","authors":"Rajani Rai B , Karunakara Rai B , Mamatha A S , Nikshitha","doi":"10.1016/j.health.2025.100435","DOIUrl":"10.1016/j.health.2025.100435","url":null,"abstract":"<div><div>Accurate classification of focal and non-focal epilepsy is a critical healthcare analytics challenge that requires robust data preprocessing and feature optimization. This work develops an integrated analytics framework that combines hybrid filtering with hybrid dimensionality reduction to improve both signal quality and predictive performance. A multi-criteria ranking strategy based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is employed, incorporating conventional signal measures alongside distance and divergence metrics to identify optimal preprocessing pipelines. Statistical validation is performed using the Friedman test with Nemenyi post-hoc analysis to establish the significance of competing filter–dimensionality reduction combinations. The validated framework is benchmarked across conventional, hybrid, and deep learning classifiers, with the most effective configuration—Butterworth.</div><div>Wavelet Packet Decomposition (BW + WPD) filtering followed by Principal Component Analysis–Linear Discriminant Analysis (PCA + LDA)—achieving 95.63% accuracy using an Adaboost classifier on the Bern–Barcelona dataset. Evaluation on the independent Bonn dataset confirms robustness and cross-subject generalizability. These findings demonstrate the value of a multi-metric, statistically validated analytics strategy for reliable epilepsy detection, with potential applicability to broader healthcare signal classification tasks.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100435"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145578578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A comparative analysis of generalized additive models for obesity risk prediction 肥胖风险预测的广义加性模型的比较分析

Healthcare analytics (New York, N.Y.) Pub Date : 2025-12-01 Epub Date: 2025-08-27 DOI: 10.1016/j.health.2025.100410

Olushina Olawale Awe , Olawale Abiodun Olaniyan , Ayorinde Emmanuel Olatunde , Ronel SewPaul , Natisha Dukhi

{"title":"A comparative analysis of generalized additive models for obesity risk prediction","authors":"Olushina Olawale Awe , Olawale Abiodun Olaniyan , Ayorinde Emmanuel Olatunde , Ronel SewPaul , Natisha Dukhi","doi":"10.1016/j.health.2025.100410","DOIUrl":"10.1016/j.health.2025.100410","url":null,"abstract":"<div><div>Obesity is a growing global health crisis, and traditional regression models often fail to capture the complex relationships between risk factors, limiting predictive accuracy and hindering effective public health interventions. Conventional methods overlook non-linear associations and interaction effects across demographic, socioeconomic, and behavioral predictors, which are particularly important in diverse populations with varying obesity determinants. To address these limitations, we applied Generalized Additive Models for Location, Scale, and Shape (GAMLSS) to analyze obesity predictors in a nationally representative adolescent sample (N <span><math><mo>=</mo></math></span> 671). Our framework included comprehensive variable selection across demographic, socioeconomic, behavioral, and clinical domains, comparison with three alternative regression models, and validation using the Generalized Akaike Information Criterion (GAIC). The binomial stepwise GAMLSS model demonstrated superior performance (GAIC <span><math><mo>=</mo></math></span> 624.98). Key findings included strong geographic variation, significant gender disparity, a socioeconomic gradient, and important behavioral predictors such as weight gain attempts. The GAMLSS framework improves obesity risk prediction by modeling complex relationships often missed by traditional methods, offering targeted intervention strategies based on geographic, gender, and socioeconomic factors, and challenging assumptions about dietary influences.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100410"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An analytical review of biosensor-based chronic pain quantification in healthcare 医疗保健中基于生物传感器的慢性疼痛量化分析综述

Healthcare analytics (New York, N.Y.) Pub Date : 2025-12-01 Epub Date: 2025-09-15 DOI: 10.1016/j.health.2025.100419

Aarthi Kannan , Daniel West , Dinesh Kumbhare , Wei-Ting Ting , Md. Younus Ali , Hameem I. Kawsar , Gurmit Singh , Harsha Shanthanna , Eleni Hapidou , Matiar M.R. Howlader

{"title":"An analytical review of biosensor-based chronic pain quantification in healthcare","authors":"Aarthi Kannan , Daniel West , Dinesh Kumbhare , Wei-Ting Ting , Md. Younus Ali , Hameem I. Kawsar , Gurmit Singh , Harsha Shanthanna , Eleni Hapidou , Matiar M.R. Howlader","doi":"10.1016/j.health.2025.100419","DOIUrl":"10.1016/j.health.2025.100419","url":null,"abstract":"<div><div>Current clinical methods for chronic pain assessment lack objective, quantitative measures, creating a critical gap in diagnostic accuracy. This review investigates the relationship between chronic pain and key biomarkers detectable in body fluids, such as glutamate, interleukin-6, nitric oxide, and quinolinic acid. We first discuss the biological mechanisms underlying chronic pain and evaluate the relevance of these biomarkers. The review then focuses on recent advancements in non-enzymatic electrochemical biosensors used to monitor these biomarkers. For each sensor, we summarize performance metrics including sensitivity, detection limits, and linear range, while highlighting the analytical methodologies used to establish correlations between biomarker levels and pain intensity. Our findings demonstrate that quantitative analysis of biomarker fluctuations can enhance chronic pain monitoring. The integration of sensor-based biomarker analytics with clinical workflows may offer a path toward personalized treatment plans and improved decision-making in healthcare supply chains. This review emphasizes the need for continued development of high-precision biosensors as analytical tools for translating physiological signals into clinically actionable pain metrics.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100419"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An analytics-driven review of U-Net for medical image segmentation U-Net用于医学图像分割的分析驱动综述

Healthcare analytics (New York, N.Y.) Pub Date : 2025-12-01 Epub Date: 2025-09-20 DOI: 10.1016/j.health.2025.100416

Fnu Neha , Deepshikha Bhati , Deepak Kumar Shukla , Sonavi Makarand Dalvi , Nikolaos Mantzou , Safa Shubbar

{"title":"An analytics-driven review of U-Net for medical image segmentation","authors":"Fnu Neha , Deepshikha Bhati , Deepak Kumar Shukla , Sonavi Makarand Dalvi , Nikolaos Mantzou , Safa Shubbar","doi":"10.1016/j.health.2025.100416","DOIUrl":"10.1016/j.health.2025.100416","url":null,"abstract":"<div><div>Medical imaging (MI) plays a vital role in healthcare by providing detailed insights into anatomical structures and pathological conditions, supporting accurate diagnosis and treatment planning. Noninvasive modalities, such as X-ray, magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US), produce high-resolution images of internal organs and tissues. The effective interpretation of these images relies on the precise segmentation of the regions of interest (ROI), including organs and lesions. Traditional methods based on manual feature extraction are time-consuming, inconsistent, and not scalable. This review explores recent advances in artificial intelligence (AI)-driven segmentation, focusing on Convolutional Neural Network (CNN) architectures, particularly the U-Net family and its variants—U-Net++, and U-Net 3+. These models enable automated, pixel-wise classification across modalities and have improved segmentation accuracy and efficiency. The review outlines the evolution of U-Net architectures, their clinical integration, and offers a modality-wise comparison. It also addresses challenges such as data heterogeneity, limited generalizability, and model interpretability, proposing solutions including attention mechanisms and Transformer-based designs. Emphasizing clinical applicability, this work bridges the gap between algorithmic development and real-world implementation.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100416"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145121178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A data-driven multicriteria decision model for healthcare workforce retention strategies 医疗保健人力保留策略的数据驱动多标准决策模型

Healthcare analytics (New York, N.Y.) Pub Date : 2025-12-01 Epub Date: 2025-06-13 DOI: 10.1016/j.health.2025.100403

Debora Di Caprio , Sofia Sironi , Fan-Yun Lan , Ramin Rostamkhani

{"title":"A data-driven multicriteria decision model for healthcare workforce retention strategies","authors":"Debora Di Caprio , Sofia Sironi , Fan-Yun Lan , Ramin Rostamkhani","doi":"10.1016/j.health.2025.100403","DOIUrl":"10.1016/j.health.2025.100403","url":null,"abstract":"<div><div>The retention of nurses and physicians in Hospitals is a global problem affecting the healthcare system worldwide. This study focuses on the healthcare workforce retention problem considering the current situation in Taiwan. Healthcare staff in Taiwan are undergoing a critical phase, with an increasing number of experienced workers leaving their job to go to work for private organizations or as freelancers. We develop a data-driven four-phase methodology based on the design of a satisfaction index that allows to rank different groups of employees against a given set of criteria. First, criteria are identified and clustered to describe different job dimensions (phase 1). Hence, subjective evaluations of the criteria are collected from healthcare workers while experts provide pairwise comparisons among them (phase 2). An adjusted analytic hierarchy process (AHP) is used to weight the job dimensions and the criteria within each job dimension (phase 3). Finally, the satisfaction index is formalized and computed for different groups of employees (phase 4). The methodology has been implemented with data collected from healthcare workers employed in three healthcare institutions in Northern Taiwan. The proposed index represents a novel decision support tool for managers and policy makers in designing intervention strategies able to address different needs of different groups of employees. Besides, it allows for innovative applications to quality management (QM) by extending the standard QM approach to hospitals and healthcare centers far beyond the common focus on patients' satisfaction. Finally, the mathematical formulation of the index is very flexible and allows for applications to any employment sector through a variety of analyses based on different categorizations of the workers.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100403"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144290808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An analytics framework for healthcare expenditure forecasting with machine learning 基于机器学习的医疗保健支出预测分析框架

Healthcare analytics (New York, N.Y.) Pub Date : 2025-12-01 Epub Date: 2025-10-28 DOI: 10.1016/j.health.2025.100428

John Wang , Shubin Xu , Yawei Wang , Houda EL Bouhissi

{"title":"An analytics framework for healthcare expenditure forecasting with machine learning","authors":"John Wang , Shubin Xu , Yawei Wang , Houda EL Bouhissi","doi":"10.1016/j.health.2025.100428","DOIUrl":"10.1016/j.health.2025.100428","url":null,"abstract":"<div><div>The United States healthcare system relies heavily on Medicaid, which serves nearly 80 million people and accounts for a substantial share of both state and federal budgets. This study employs a range of forecasting methods, including ARIMA, Holt's linear trend, polynomial regressions (degree 2 and 4), Prophet, and piecewise linear regression, as well as machine learning models such as random forest, gradient boosting, and support vector regression, to analyze the growth of Medicaid expenditures. Using data from 1966 to 2024, the analysis identifies historical patterns and evaluates model performance with Root Mean Squared Error (RMSE) and related metrics to project costs through 2035. The results show that the autoregressive model with integrated moving average and Prophet generate the most accurate baseline forecasts, suggesting that Medicaid expenditures are likely to exceed one trillion dollars within the next 15 years. Although the machine learning models produced somewhat lower estimates, they revealed complex relationships between policy variables and expenditure behavior, making them useful for building alternative forecasting scenarios. The discussion emphasizes the policy relevance of these findings, particularly in relation to budget sustainability and healthcare equity, and highlights the importance of employing multiple forecasting approaches. Overall, the study demonstrates the value of decision analytics in healthcare forecasting by highlighting the need for accurate predictions, flexible models, and interpretable outcomes. It provides evidence-based tools to anticipate Medicaid's financial challenges and support the development of sustainable healthcare strategies for the years ahead.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100428"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145519215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A machine learning framework for predicting healthcare utilization and risk factors 用于预测医疗保健利用和风险因素的机器学习框架

Healthcare analytics (New York, N.Y.) Pub Date : 2025-12-01 Epub Date: 2025-08-19 DOI: 10.1016/j.health.2025.100411

Yead Rahman , Prerna Dua

{"title":"A machine learning framework for predicting healthcare utilization and risk factors","authors":"Yead Rahman , Prerna Dua","doi":"10.1016/j.health.2025.100411","DOIUrl":"10.1016/j.health.2025.100411","url":null,"abstract":"<div><div>Medicaid data, with its vast scale and heterogeneity, presents significant challenges in predictive modeling and healthcare analytics. This study analyzes over 6.3 million records from the Louisiana Department of Health (LDH) to identify the most effective machine learning models for predicting clinical service utilization, COVID-19 infections, and tobacco use. A rigorous preprocessing pipeline ensured data integrity, while exploratory data analysis (EDA) guided feature selection, ultimately retaining 20 key variables to capture complex interactions. Seven supervised models, i.e., logistic regression, extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), random forest, decision tree, artificial neural networks (ANN), and naïve bayes, were evaluated based on predictive performance, computational efficiency, and feature importance. While ensemble methods such as XGBoost and random forest achieved superior accuracy, their high computational demands highlight the trade-off between performance and efficiency in large-scale healthcare analytics. Simpler models like naïve bayes and decision trees were computationally efficient but less accurate. Key predictors included hospital stay duration for healthcare service utilization, tobacco use for COVID-19 risk, and chronic obstructive pulmonary disease (COPD) for tobacco use. These findings emphasize the impact of comorbidities and demographics on healthcare utilization, offering data-driven insights for healthcare practitioners and policymakers to enhance patient care, optimize costs, and refine policy decisions.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100411"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144885885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EAGLE-Net: A hierarchical neural network for detecting anatomical landmarks in upper gastrointestinal endoscopy for clinical diagnosis EAGLE-Net：一种用于检测上消化道内镜解剖标志的分层神经网络，用于临床诊断

Healthcare analytics (New York, N.Y.) Pub Date : 2025-12-01 Epub Date: 2025-09-20 DOI: 10.1016/j.health.2025.100420

Thi Thu Huong Nguyen , Van Duy Truong , Xuan Huy Manh , Thanh Tung Nguyen , Hang Viet Dao , Hai Vu

{"title":"EAGLE-Net: A hierarchical neural network for detecting anatomical landmarks in upper gastrointestinal endoscopy for clinical diagnosis","authors":"Thi Thu Huong Nguyen , Van Duy Truong , Xuan Huy Manh , Thanh Tung Nguyen , Hang Viet Dao , Hai Vu","doi":"10.1016/j.health.2025.100420","DOIUrl":"10.1016/j.health.2025.100420","url":null,"abstract":"<div><div>This study proposes a hierarchical network architecture, named EAGLE-Net, for identifying anatomical landmarks in the upper gastrointestinal (GI) tract endoscopic videos. Unlike conventional techniques, which label anatomical landmarks for static endoscopic images, the proposed method aims to classify landmarks from videos of the upper GI tract. Video streams often suffer from many noises and contaminated objects, which requires a new approach to tackle this issue. The proposed technique utilizes hierarchical network architecture, which consists of two stages: endoscopic image quality assessment and anatomical landmark classification. In the first stage, high-quality frames are preserved from GI tract videos. These frames are then used to identify a specific location among ten anatomical landmarks. The proposed method increases the coherence between the hierarchical data levels. It integrates an attention module to strengthen feature connections and utilizes a new hierarchical cross-entropy loss function to optimize model performance. The experimental results demonstrated that the proposed system achieves a high accuracy of 93% on average in both classification stages. In clinical experiments, anatomical landmarks are automatically denoted to help physicians monitor the endoscopy process. In addition, the proposed method demonstrates a potential solution for the deployment of a computer-aided diagnostic application for the detection and treatment of upper GI tract lesions.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100420"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145157325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A multi-agent reinforcement learning framework for public health decision analysis 公共卫生决策分析的多智能体强化学习框架

Healthcare analytics (New York, N.Y.) Pub Date : 2025-12-01 Epub Date: 2025-11-20 DOI: 10.1016/j.health.2025.100436

Dinesh Sharma , Ankit Shah , Chaitra Gopalappa

{"title":"A multi-agent reinforcement learning framework for public health decision analysis","authors":"Dinesh Sharma , Ankit Shah , Chaitra Gopalappa","doi":"10.1016/j.health.2025.100436","DOIUrl":"10.1016/j.health.2025.100436","url":null,"abstract":"<div><div>Human immunodeficiency virus (HIV) is a major public health concern in the United States (U.S.), with about 1.2 million people living with it and about 35,000 newly infected each year. There are considerable geographical disparities in HIV burden and care access across the U.S. The ’Ending the HIV Epidemic (EHE)’ initiative by the U.S. Department of Health and Human Services aims to reduce new infections by 90% by 2030, by improving coverage of diagnoses, treatment, and prevention interventions and prioritizing jurisdictions with high HIV prevalence. One of the approaches towards achieving this objective includes developing intelligent decision-support systems that can help optimize resource allocation and intervention strategies. Existing decision analytic models either focus on individual cities or aggregate national data, failing to capture jurisdictional interactions critical for optimizing intervention strategies. To address this, we propose a multi-agent reinforcement learning (MARL) framework that enables jurisdiction-specific decision-making while accounting for cross-jurisdictional epidemiological interactions. Our framework functions as an intelligent resource optimization system, helping policymakers strategically allocate interventions based on dynamic, data-driven insights. Experimental results across jurisdictions in California and Florida demonstrate that MARL-driven policies outperform traditional single-agent reinforcement learning approaches by reducing new infections under fixed budget constraints. Our study highlights the importance of incorporating jurisdictional dependencies in decision-making frameworks for large-scale public initiatives. By integrating multi-agent intelligent systems, decision analytics, and reinforcement learning, this study advances expert systems for government resource planning and public health management, offering a scalable framework for broader applications in healthcare policy and epidemic management.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100436"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A deep learning framework for automated breast cancer diagnosis using intelligent segmentation and classification 一种使用智能分割和分类的自动化乳腺癌诊断的深度学习框架

Healthcare analytics (New York, N.Y.) Pub Date : 2025-12-01 Epub Date: 2025-08-30 DOI: 10.1016/j.health.2025.100414

Ahed Abugabah

{"title":"A deep learning framework for automated breast cancer diagnosis using intelligent segmentation and classification","authors":"Ahed Abugabah","doi":"10.1016/j.health.2025.100414","DOIUrl":"10.1016/j.health.2025.100414","url":null,"abstract":"<div><div>Breast cancer is the most commonly diagnosed cancer among women worldwide, accounting for a significant proportion of new cases. Deep learning (DL) has emerged as a powerful tool for the detection and diagnosis of breast cancer, particularly through the analysis of histological images, a critical component of automated diagnostic systems that directly impact patient management. The BreakHis dataset and the Wisconsin Breast Cancer Database (WBCD) are widely used publicly available resources for deep learning–based analyses of breast cancer histological images in cross-disciplinary healthcare research. A computer-assisted approach employs colour normalisation to reduce the effects of the differences in the distribution of breast histopathology images. In this paper, breast tumour areas of interest are segmented utilising Attention-Guided Deep Atrous-Residual U-Net at the segmentation stage. Subsequently, patches are processed to form feature vectors VGG19 and ResNet50 for the extraction of deep features from the patches. Also, to fine-tune these models even further, the breast cancer datasets are employed, and Levy Flight-based Red Fox Optimisation is used to extract features from the pre-trained models without further training. The Efficient Capsule Network is used to improve the feature representation and classification capabilities. AGDATUNet-LFRFO-ECN, which was suggested in the study, performed better than other models when tested on the WBCD dataset, with a sensitivity of 99.17 %, specificity of 99.08 %, and accuracy of 99.23 %. What's more, the AGDATUNet-LFRFO-ECN outperformed the available models on BreakHis with a sensitivity of 99.81 %, a specificity of 99.79 %, and an accuracy of 99.82 %, which are the state-of-the-art.</div></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"8 ","pages":"Article 100414"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0