Intelligence-based medicine最新文献_第6页

Speech biomarkers predict amyloid status in cognitively unimpaired adults 语言生物标志物预测认知功能正常的成年人的淀粉样蛋白状态

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100306

Peru Gabirondo , María García-Martínez , Ana Pozueta-Cantudo , Patricia Laura Maran , Patricia Dias , Tomas Rojo , Javier Jiménez-Raboso , Carmen Lage , Francisco Martínez-Dubarbie , Sara López-García , Marta Fernández-Matarrubia , Andrea Corrales-Pardo , María Bravo , Juan Irure-Ventura , Marcos López-Hoyos , Pascual Sánchez-Juan , Carla Zaldua , Eloy Rodríguez-Rodríguez

引用次数: 0

Enhancing emotion recognition through multi-modal data fusion and graph neural networks 通过多模态数据融合和图神经网络增强情绪识别

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100291

Kasthuri Devarajan , Suresh Ponnan , Sundresan Perumal

In this paper, a novel emotion detection system is proposed based on Graph Neural Network (GNN) architecture, which is used to integrate and learn from multiple data sets (EEG, face expression, physiological signals). The proposed GNN is able to learn about interactions between multiple modalities, so as to extract a single picture of emotion categorization. This model is very good and gets 91.25 % accuracy, 91.26 % precision, 91.25 % recall and 91.25 % F1-score. Moreover, the proposed GNN is a sensible trade-off between speed and precision, with a calculation time of 163 ms. The Proposed GNN is better, primarily due to its ability to represent complex relations between multi-modal inputs, thereby improving its real-time emotional state recognition and classification performance. The proposed GNN demonstrates its suitability for powerful emotion detection by outperforming all models in classification precision and multi-modal data fusion, surpassing traditional models such as SVM, KNN, CCA, CNN, and RNN. The Proposed GNN consistently proves to be the most accurate and robust solution, having been the most dominant technique in emotion detection, despite CNN and RNN achieving slightly better results.

本文提出了一种基于图神经网络（GNN）架构的情绪检测系统，该系统用于对多个数据集（EEG、面部表情、生理信号）进行整合和学习。提出的GNN能够学习多个模态之间的相互作用，从而提取出单一的情绪分类图像。该模型的准确率为91.25%，精密度为91.26%，召回率为91.25%，f1分数为91.25%。此外，所提出的GNN在速度和精度之间进行了合理的权衡，计算时间为163 ms。提出的GNN更好，主要是因为它能够表示多模态输入之间的复杂关系，从而提高了其实时情绪状态识别和分类性能。本文提出的GNN在分类精度和多模态数据融合方面优于所有模型，超越了SVM、KNN、CCA、CNN和RNN等传统模型，证明了它适合于强大的情感检测。尽管CNN和RNN取得了稍好的结果，但所提出的GNN始终被证明是最准确和鲁棒的解决方案，是情绪检测中最主要的技术。

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引用次数: 0

Clinical-ready CNN framework for lung cancer classification: Systematic optimization for healthcare deployment with enhanced computational efficiency 用于肺癌分类的临床就绪CNN框架：提高计算效率的医疗部署系统优化

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100292

G. Inbasakaran, J. Anitha Ruth

Purpose

This study develops a computationally efficient Convolutional Neural Network (CNN) for lung cancer classification in Computed Tomography (CT) images, addressing the critical need for accurate diagnostic tools deployable in resource-constrained clinical settings.

Methods

Using the IQ-OTH/NCCD dataset (1190 CT images: normal, benign, and malignant classes from 110 patients), we implemented systematic architecture optimization with strategic data augmentation to address class imbalance and limited dataset challenges. Patient-level data splitting prevented leakage, ensuring valid performance metrics. The model was evaluated using 5-fold cross-validation and compared against established architectures using McNemar's test for statistical significance.

Results

The optimized CNN achieved 94 % classification accuracy with only 4.2 million parameters and 18 ms inference time. Performance significantly exceeded AlexNet (85 %), VGG-16 (88 %), ResNet-50 (90 %), InceptionV3 (87 %), and DenseNet (86 %) with p < 0.05. Malignant case detection showed excellent clinical metrics (precision: 0.96, recall: 0.95, F1-score: 0.95), critical for minimizing false negatives. Ablation studies revealed data augmentation contributed 6.6 % accuracy improvement, with rotation and translation proving most effective. The model operates 4.3 × faster than ResNet-50 while using 6 × fewer parameters, enabling deployment on standard clinical workstations with 4–8 GB GPU memory.

Conclusions

Carefully optimized CNN architectures can achieve superior diagnostic performance while meeting computational constraints of real-world medical settings. Our approach demonstrates that systematic optimization strategies effectively balance accuracy with clinical deployment feasibility, providing a practical framework for implementing AI-assisted lung cancer detection in resource-limited healthcare environments. The model's high sensitivity for malignant cases positions it as a valuable clinical decision support tool.

目的：本研究开发了一种计算效率高的卷积神经网络（CNN），用于计算机断层扫描（CT）图像中的肺癌分类，解决了在资源有限的临床环境中部署准确诊断工具的关键需求。方法利用iqoth /NCCD数据集（来自110例患者的1190张CT图像：正常、良性和恶性分类），通过战略性数据增强实现系统架构优化，以解决分类不平衡和数据集有限的挑战。患者级数据分割防止了泄漏，确保了有效的性能指标。该模型使用5倍交叉验证进行评估，并使用McNemar的统计显著性检验与已建立的架构进行比较。结果优化后的CNN只需要420万个参数和18 ms的推理时间，分类准确率达到94%。性能显著高于AlexNet（85%）、VGG-16（88%）、ResNet-50（90%）、InceptionV3（87%）和DenseNet (86%), p < 0.05。恶性病例的检出表现出优异的临床指标（准确率：0.96，召回率：0.95，f1评分：0.95），这对于减少假阴性至关重要。消融研究显示，数据增强提高了6.6%的准确性，旋转和平移被证明是最有效的。该模型的运行速度比ResNet-50快4.3倍，同时使用的参数减少了6倍，可在具有4-8 GB GPU内存的标准临床工作站上部署。结论经过精心优化的CNN架构在满足现实医疗环境计算约束的情况下，能够取得优异的诊断性能。我们的方法表明，系统优化策略有效地平衡了准确性和临床部署的可行性，为在资源有限的医疗环境中实施人工智能辅助肺癌检测提供了一个实用的框架。该模型对恶性病例的高敏感性使其成为一种有价值的临床决策支持工具。

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引用次数: 0

A drug recommendation system based on response prediction: Integrating gene expression and K-mer fragmentation of drug SMILES using LightGBM 基于反应预测的药物推荐系统：利用LightGBM整合药物SMILES的基因表达和K-mer碎片化

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100206

Sajid Naveed , Mujtaba Husnain

Medical experts and physicians examine the gene expression abnormality in glioblastoma (GBM) cancer patients to identify the drug response. The main objective of this research is to build a machine learning (ML) based model for improve the outcome of cancer medication to save the time and effort of medical practitioners. Developing a drug response recommendation system is our goal that uses the gene expression data of cancer cell lines to predict the response of anticancer drugs in terms of half-maximal inhibitory concentration (IC50). Genetic data from a GBM cancer patient is used as input into a system to predict and recommend the response of multiple anticancer drugs in a particular cancer sample. In this research, we used K-mer molecular fragmentation to process drug SMILES in a novel way, which enabled us to build a competent model that provides drug response. We used the Light Gradient Boosting Machine (LightGBM) regression algorithm and Genomics of Drug Sensitivity of Cancer (GDSC) data for this proposed recommendation system. The results showed that all predicted IC50 values are fall within the range of the real values when examining GBM data. Two drugs, temozolomide and carmustine, were predicted with a Mean Squared Error (MSE) of 0.10 and 0.11 respectively, and 0.41 in unseen test samples. These recommended responses were then verified by expert doctors, who confirmed that the responses to these drugs were very close to the actual response. These recommendation are also effective in slowing the growth of these tumors and improving patients quality of life by monitoring medication effects.

医学专家和医生检查胶质母细胞瘤（GBM）癌症患者的基因表达异常，以确定药物反应。本研究的主要目的是建立一个基于机器学习（ML）的模型，以改善癌症药物治疗的结果，从而节省医生的时间和精力。我们的目标是开发一种药物反应推荐系统，利用癌细胞系的基因表达数据，以半最大抑制浓度（IC50）来预测抗癌药物的反应。来自GBM癌症患者的遗传数据被用作系统的输入，以预测和推荐多种抗癌药物对特定癌症样本的反应。在这项研究中，我们利用K-mer分子碎片以一种新颖的方式处理药物SMILES，这使我们能够建立一个提供药物反应的胜任模型。我们使用光梯度增强机（Light Gradient Boosting Machine, LightGBM）回归算法和癌症药物敏感性基因组学（Genomics of Drug Sensitivity of Cancer， GDSC）数据来构建这个推荐系统。结果表明，对GBM数据的预测IC50值均落在实际值的范围内。替莫唑胺和卡莫司汀两种药物的预测均方误差（MSE）分别为0.10和0.11，未见样品的预测均方误差为0.41。这些建议的反应然后由专家医生验证，他们确认对这些药物的反应非常接近实际反应。这些建议也有效地减缓这些肿瘤的生长，并通过监测药物效果来改善患者的生活质量。

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引用次数: 0

Arkangel AI: A conversational agent for real-time, evidence-based medical question-answering Arkangel AI：实时、循证医学问答的对话代理

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100274

Maria Camila Villa, Natalia Castano-Villegas, Isabella Llano, Julian Martinez, Maria Fernanda Guevara, Jose Zea, Laura Velásquez

Introduction

Large Language Models (LLMs) have been trained and tested on several medical question-answering (QA) datasets built from medical licensing exams and natural interactions between doctors and patients to fine-tune them for specific health-related tasks.

Objective

We aimed to develop LLM-powered Conversational Agents (CAs) equipped to produce fast, accurate, and real-time responses to medical queries in different clinical and scientific scenarios. This paper presents Arkangel AI, our first conversational agent and research assistant.

Methods

The model is based on a system containing five LLMs; each is classified within a specific workflow with pre-defined instructions to produce the best search strategy and provide evidence-based answers. We assessed accuracy, intra/inter-class variability, and Cohen's Kappa using the question-answer (QA) dataset MedQA. Additionally, we used the PubMedQA dataset and assessed both databases using the RAGAS framework, including Context, Response Relevance, and Faithfulness. Traditional statistical analysis was performed with hypothesis tests and 95 % IC.

Results

Accuracy for MedQA (n: 1273) was 90.26 % and Cohen's kappa was 87 %, surpassing current SoTAs for other LLMs (GPT-4o, MedPaLM2). The model retrieved 80 % of the expected articles and provided relevant answers in 82 % of PubMedQA.

Conclusion

Arkangel AI showed proficient retrieval and reasoning abilities and unbiased responses. Evenly distributed medical QA datasets to train improved LLMs and external validation for the model with real-world physicians in clinical scenarios are needed. Clinical decision-making remains in the hands of trained healthcare professionals.

大型语言模型（llm）已经在几个医学问答（QA）数据集上进行了培训和测试，这些数据集来自医疗许可考试和医生和患者之间的自然互动，以微调它们以适应特定的健康相关任务。我们的目标是开发基于llm的会话代理（CAs），以便在不同的临床和科学场景中对医疗查询产生快速、准确和实时的响应。本文介绍了Arkangel AI，我们的第一个会话代理和研究助理。方法基于一个包含5个llm的系统建立模型；每个都在特定的工作流中进行分类，并带有预定义的指令，以产生最佳搜索策略并提供基于证据的答案。我们使用问答（QA）数据集MedQA评估准确性、类内/类间变异性和Cohen Kappa。此外，我们使用PubMedQA数据集，并使用RAGAS框架评估两个数据库，包括上下文、响应相关性和可信度。结果MedQA （n: 1273）的准确率为90.26%,Cohen’s kappa为87%，超过了目前其他LLMs （gpt - 40、MedPaLM2）的SoTAs。该模型检索了80%的预期文章，并在82%的PubMedQA中提供了相关答案。结论arkangel人工智能具有良好的检索推理能力和无偏性反应。需要均匀分布的医疗QA数据集来训练改进的llm，并在临床场景中与现实世界的医生一起对模型进行外部验证。临床决策仍然掌握在训练有素的医疗保健专业人员手中。

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引用次数: 0

Design and implementation of a low-cost malaria diagnostic system based on convolutional neural network 基于卷积神经网络的低成本疟疾诊断系统的设计与实现

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100272

Ekobo Akoa Brice , Ndoumbe Jean , Mohamadou Madina

This work focuses on the design and implementation of an intelligent system that can diagnose malaria from blood smear images. This system takes data in the image format and provides an instant and automated diagnosis to output the result of the patient’s condition on a screen. The methodology for achieving the system is based on the CNN (convolutional neural network). The latter has the specificity to function as a feature extractor and image classifier. The software part thus obtained is implemented in an electronic device that serves as a kit mounted with our care. The establishment of such a system has innumerable assets, such as rapidity during diagnosis by a laboratory technician or not; its portability that will facilitate its use wherever needed. From an ergonomic and functional point of view, the system has a real impact in the diagnosis of a large-scale malaria endemic. The CNN was trained on a large dataset of blood smears and was able to accurately classify infected and uninfected samples with high sensitivity and specificity. Insofar as the system carried out after testing on several samples reaches an average sensitivity of 89.50% and an average precision of 89%, this improves decision-making on the diagnosis of malaria. The system thus created allows malaria to be diagnosed at low cost from blood smear images. The use of CNNs in this project has the advantage of automatically extracting features from blood smear images and classifying them efficiently. The major advantage of the proposed system is its portability and lower cost. The performance of the proposed algorithm was evaluated on a publicly available malaria data set.

这项工作的重点是设计和实现一个智能系统，可以从血液涂片图像诊断疟疾。该系统以图像格式获取数据，并提供即时和自动的诊断，将患者的病情结果输出到屏幕上。实现该系统的方法是基于CNN（卷积神经网络）。后者具有作为特征提取器和图像分类器的特异性。由此获得的软件部分在一个电子设备中实现，该设备作为一个工具包安装在我们的护理中。建立这样一个系统具有无数的优势，例如实验室技术人员在诊断过程中是否快速；它的可移植性将使它在任何需要的地方都能使用。从人体工程学和功能的角度来看，该系统对大规模疟疾地方病的诊断具有实际影响。CNN在一个大型的血液涂片数据集上进行训练，能够以高灵敏度和特异性准确地对感染和未感染的样本进行分类。在对几个样本进行测试后，该系统的平均灵敏度达到89.50%，平均精度达到89%，这改善了疟疾诊断的决策。这样创建的系统可以通过血液涂片图像以低成本诊断疟疾。在本课题中使用cnn具有从血液涂片图像中自动提取特征并进行高效分类的优点。该系统的主要优点是可移植性和低成本。在一个公开可用的疟疾数据集上评估了所提出算法的性能。

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引用次数: 0

Meta-learning driven multi disease fuzzy neural framework for clinical risk prediction 基于元学习驱动的多疾病模糊神经框架临床风险预测

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100315

Kubra Noor, Ubaida Fatima, Fahim Raees

Rising rates of chronic illnesses, including heart disease, diabetes, and cancer, demand precise and scalable early diagnostic approaches. Utilizing three datasets UCI Heart Disease, PIMA Indians Diabetes, and Breast Cancer Wisconsin we suggest a metalearning-inspired hybrid approach combining Fuzzy C-Means clustering and artificial neural networks for multidisease risk prediction. Fuzzy logic is used to cluster each dataset to model intraclass variation; cluster-specific neural networks are then trained to catch patterns. Fuzzy membership ratings are used to combine final forecasts. Achieving 85.25 % (heart disease), 81.2 % (diabetes), and 95.1 % (cancer) accuracy, respectively, the suggested system shows great accuracy, disease-wide generalization, and interpretability. The results show improved predictions for complex and varied patient profiles, confirming that the system is strong and useful for real-world health analysis.

包括心脏病、糖尿病和癌症在内的慢性病发病率不断上升，需要精确和可扩展的早期诊断方法。利用UCI心脏病、PIMA印第安人糖尿病和威斯康星州乳腺癌三个数据集，我们提出了一种元学习启发的混合方法，将模糊c均值聚类和人工神经网络相结合，用于多疾病风险预测。利用模糊逻辑对各数据集进行聚类，模拟类内变化；然后训练特定于集群的神经网络来捕捉模式。模糊隶属度评级用于组合最终预测。该系统分别达到85.25%（心脏病）、81.2%（糖尿病）和95.1%（癌症）的准确率，显示出很高的准确性、疾病通用性和可解释性。结果表明，对复杂和不同的患者概况的预测有所改善，证实了该系统在现实世界的健康分析中是强大和有用的。

引用次数: 0

Feature selection using hybridized Genghis Khan Shark with snow ablation optimization technique for multi-disease prognosis 成吉思汗鲨杂交特征选择与雪消融优化技术在多疾病预后中的应用

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100249

Ruqsar Zaitoon , Shaik Salma Asiya Begum , Sachi Nandan Mohanty , Deepa Jose

The exponential growth in medical data and feature dimensionality presents significant challenges in building accurate and efficient diagnostic models. High-dimensional datasets often contain redundant or irrelevant features that degrade classification performance and increase computational burden. Feature selection (FS) is therefore a critical step in medical data analysis to enhance model accuracy and interpretability. While many recent FS techniques rely on optimization algorithms, tuning their parameters and avoiding early convergence remain major challenges. This study introduces a novel hybrid optimization technique—Hybridized Genghis Khan Shark with Snow Ablation Optimization (HyGKS-SAO)—to identify the most informative features for multi-disease classification. The raw medical datasets are first pre-processed using a Tanh-based normalization method. The HyGKS-SAO algorithm then selects optimal features, balancing exploration and exploitation effectively. Finally, a multi-kernel support vector machine (SVM) is employed to classify diseases based on the selected features. The proposed framework is evaluated on six publicly available medical datasets, including breast cancer, diabetes, heart disease, stroke, lung cancer, and chronic kidney disease. Experimental results demonstrate the effectiveness of the proposed method, achieving 98 % accuracy, 97.99 % MCC, 96.31 % PPV, 97.35 % G-mean, 98.03 % Kappa Coefficient, and a low computation time of 50 s, outperforming several state-of-the-art approaches.

医疗数据和特征维数的指数级增长为建立准确、高效的诊断模型提出了重大挑战。高维数据集通常包含冗余或不相关的特征，这些特征会降低分类性能并增加计算负担。因此，特征选择（FS）是医疗数据分析中提高模型准确性和可解释性的关键步骤。虽然许多最新的FS技术依赖于优化算法，但调整其参数和避免早期收敛仍然是主要挑战。本研究引入一种新的混合优化技术-杂交成吉思汗鲨鱼与雪消融优化(HyGKS-SAO) -来识别最具信息量的特征，用于多疾病分类。首先使用基于tanh的规范化方法对原始医疗数据集进行预处理。HyGKS-SAO算法选择最优特征，有效地平衡了搜索和开发。最后，利用多核支持向量机（SVM）对所选特征进行疾病分类。拟议的框架在六个公开可用的医疗数据集上进行了评估，包括乳腺癌、糖尿病、心脏病、中风、肺癌和慢性肾病。实验结果证明了该方法的有效性，准确率为98%，MCC为97.99%，PPV为96.31%，g均值为97.35%，Kappa系数为98.03%，计算时间仅为50 s，优于几种最先进的方法。

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引用次数: 0

AI speechbots and 3D segmentations in virtual reality improve radiology on-call training in resource-limited settings 人工智能语音机器人和虚拟现实中的3D分割改善了资源有限环境下的放射学随叫随到培训

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100245

Yusuf Alibrahim , Muhieldean Ibrahim , Devindra Gurdayal , Muhammad Munshi

Objective

Evaluate the use of large-language model (LLM) speechbot tools and deep learning-assisted generation of 3D reconstructions when integrated in a virtual reality (VR) setting to teach radiology on-call topics to radiology residents.

Methods

Three first year radiology residents in Guyana were enrolled in an 8-week radiology course that focused on preparation for on-call duties. The course, delivered via VR headsets with custom software integrating LLM-powered speechbots trained on imaging reports and 3D reconstructions segmented with the help of a deep learning model. Each session focused on a specific radiology area, employing a didactic and case-based learning approach, enhanced with 3D reconstructions and an LLM-powered speechbot. Post-session, residents reassessed their knowledge and provided feedback on their VR and LLM-powered speechbot experiences.

Results/discussion

Residents found that the 3D reconstructions segmented semi-automatically by deep learning algorithms and AI-driven self-learning via speechbot was highly valuable. The 3D reconstructions, especially in the interventional radiology session, were helpful and the benefit is augmented by VR where navigating the models is seamless and perception of depth is pronounced. Residents also found conversing with the AI-speechbot seamless and was valuable in their post session self-learning. The major drawback of VR was motion sickness, which was mild and improved over time.

Conclusion

AI-assisted VR radiology education could be used to develop new and accessible ways of teaching a variety of radiology topics in a seamless and cost-effective way. This could be especially useful in supporting radiology education remotely in regions which lack local radiology expertise.

目的评估在虚拟现实（VR）环境中集成大语言模型（LLM）语音机器人工具和深度学习辅助生成3D重建的使用，以教授放射科住院医师放射学随叫随到的主题。方法对圭亚那3名一年级放射科住院医师进行为期8周的放射学培训，重点是为随叫随到的工作做准备。该课程通过VR头显和定制软件提供，集成了llm支持的语音机器人，这些语音机器人接受过成像报告和3D重建的培训，并借助深度学习模型进行分割。每次会议都集中在一个特定的放射学领域，采用教学和基于案例的学习方法，通过3D重建和llm驱动的语音机器人进行增强。课程结束后，学员们重新评估了他们的知识，并就他们的VR和llm语音机器人体验提供了反馈。结果/讨论居民发现，通过深度学习算法和人工智能驱动的语音机器人自主学习进行半自动分割的三维重建非常有价值。3D重建，特别是在介入放射学会话中，是有帮助的，VR增强了这种好处，其中导航模型是无缝的，深度感知是明显的。居民们还发现，与人工智能语音机器人的对话是无缝的，在他们的课后自学中很有价值。VR的主要缺点是晕动病，这是轻微的，随着时间的推移会改善。结论人工智能辅助的虚拟现实放射学教学可以为各种放射学主题的教学提供新的、可访问的、无缝的、高性价比的教学方式。这对于在缺乏当地放射专业知识的地区支持远程放射学教育尤其有用。

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引用次数: 0

Deep learning based detection of endometriosis lesions in laparoscopic images with 5-fold cross-validation 基于深度学习的腹腔镜子宫内膜异位症病变检测及5倍交叉验证

Intelligence-based medicine

Pub Date : 2025-01-01 DOI: 10.1016/j.ibmed.2025.100230

Shujaat Ali Zaidi , Varin Chouvatut , Chailert Phongnarisorn , Dussadee Praserttitipong

Endometriosis, a complex gynecological condition, presents significant diagnostic challenges due to the subtle and varied appearance of its lesions. This study leverages deep learning to classify endometriosis lesions in laparoscopic images using the Gynecologic Laparoscopy Endometriosis Dataset (GLENDA). Three deep learning models VGG19, ResNet50, and Inception V3 were trained and evaluated with 5-fold cross-validation to enhance generalizability and mitigate overfitting. Robust data augmentation techniques were applied to address dataset limitations. The models were tasked with classifying lesions into pathological and nonpathological categories. Experimental results demonstrated strong performance, with VGG19, ResNet50, and Inception V3 achieving accuracies of 0.89, 0.91, and 0.93, respectively. Inception V3 outperformed the others, highlighting its efficacy for this task. The findings underscore the potential of deep learning in improving endometriosis diagnosis, offering a reliable tool for clinicians. This study contributes to the growing field of AI-driven medical image analysis, emphasizing the value of cross-validation and data augmentation in enhancing model performance for specialized medical applications.

子宫内膜异位症是一种复杂的妇科疾病，由于其病变的微妙和多样的外观，提出了重大的诊断挑战。本研究利用妇科腹腔镜子宫内膜异位症数据集（GLENDA），利用深度学习对腹腔镜图像中的子宫内膜异位症病变进行分类。三个深度学习模型VGG19， ResNet50和Inception V3进行了训练和评估，并进行了5倍交叉验证，以增强泛化性并减少过拟合。应用稳健的数据增强技术来解决数据集的局限性。这些模型的任务是将病变分为病理和非病理两类。实验结果显示了较强的性能，VGG19、ResNet50和Inception V3的准确率分别为0.89、0.91和0.93。Inception V3的表现优于其他版本，突出了它在此任务中的有效性。研究结果强调了深度学习在改善子宫内膜异位症诊断方面的潜力，为临床医生提供了可靠的工具。这项研究促进了人工智能驱动的医学图像分析领域的发展，强调了交叉验证和数据增强在提高专业医疗应用的模型性能方面的价值。

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引用次数: 0