Health Information Science and Systems最新文献_第3页

Rapid detection and interpretation of heart murmurs using phonocardiograms, transfer learning and explainable artificial intelligence. 利用语音心电图、迁移学习和可解释人工智能快速检测和解释心脏杂音。

IF 4.4 3区医学

Health Information Science and Systems Pub Date : 2024-08-24 eCollection Date: 2024-12-01 DOI: 10.1007/s13755-024-00302-w

Fatma Özcan

{"title":"Rapid detection and interpretation of heart murmurs using phonocardiograms, transfer learning and explainable artificial intelligence.","authors":"Fatma Özcan","doi":"10.1007/s13755-024-00302-w","DOIUrl":"10.1007/s13755-024-00302-w","url":null,"abstract":"Cardiovascular disease, which remains one of the main causes of death, can be prevented by early diagnosis of heart sounds. Certain noisy signals, known as murmurs, may be present in heart sounds. On auscultation, the degree of murmur is closely related to the patient's clinical condition. Computer-aided decision-making systems can help doctors to detect murmurs and make faster decisions. The Mel spectrograms were generated from raw phonocardiograms and then presented to the OpenL3 network for transfer learning. In this way, the signals were classified to predict the presence or absence of murmurs and their level of severity. Pitch level (healthy, low, medium, high) and Levine scale (healthy, soft, loud) were used. The results obtained without prior segmentation are very impressive. The model used was then interpreted using an Explainable Artificial Intelligence (XAI) method, Occlusion Sensitivity. This approach shows that XAI methods are necessary to know the features used internally by the artificial neural network then to explain the automatic decision taken by the model. The averaged image of the occlusion sensitivity maps can give us either an overview or a precise detail per pixel of the features used. In the field of healthcare, particularly cardiology, for rapid diagnostic and preventive purposes, this work could provide more detail on the important features of the phonocardiogram.","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"12 1","pages":"43"},"PeriodicalIF":4.4,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11344737/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142074193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimized automated detection of diabetic retinopathy severity: integrating improved multithresholding tunicate swarm algorithm and improved hybrid butterfly optimization. 糖尿病视网膜病变严重程度的优化自动检测：集成改进的多阈值调谐蜂群算法和改进的混合蝴蝶优化算法。

IF 3.4 3区医学

Health Information Science and Systems Pub Date : 2024-08-12 eCollection Date: 2024-12-01 DOI: 10.1007/s13755-024-00301-x

Usharani Bhimavarapu

{"title":"Optimized automated detection of diabetic retinopathy severity: integrating improved multithresholding tunicate swarm algorithm and improved hybrid butterfly optimization.","authors":"Usharani Bhimavarapu","doi":"10.1007/s13755-024-00301-x","DOIUrl":"10.1007/s13755-024-00301-x","url":null,"abstract":"Diabetic retinopathy, a complication of diabetes, damages the retina due to prolonged high blood sugar levels, leading to vision impairment and blindness. Early detection through regular eye exams and proper diabetes management are crucial in preventing vision loss. DR is categorized into five classes based on severity, ranging from no retinopathy to proliferative diabetic retinopathy. This study proposes an automated detection method using fundus images. Image segmentation divides fundus images into homogeneous regions, facilitating feature extraction. Feature selection aims to reduce computational costs and improve classification accuracy by selecting relevant features. The proposed algorithm integrates an Improved Tunicate Swarm Algorithm (ITSA) with Renyi's entropy for enhanced adaptability in the initial and final stages. An Improved Hybrid Butterfly Optimization (IHBO) Algorithm is also introduced for feature selection. The effectiveness of the proposed method is demonstrated using retinal fundus image datasets, achieving promising results in DR severity classification. For the IDRiD dataset, the proposed model achieves a segmentation Dice coefficient of 98.06% and classification accuracy of 98.21%. In contrast, the E-Optha dataset attains a segmentation Dice coefficient of 97.95% and classification accuracy of 99.96%. Experimental results indicate the algorithm's ability to accurately classify DR severity levels, highlighting its potential for early detection and prevention of diabetes-related blindness.","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"12 1","pages":"42"},"PeriodicalIF":3.4,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11319704/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141983523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comprehensive applications of the artificial intelligence technology in new drug research and development. 人工智能技术在新药研发中的全面应用。

IF 3.4 3区医学

Health Information Science and Systems Pub Date : 2024-08-08 eCollection Date: 2024-12-01 DOI: 10.1007/s13755-024-00300-y

Hongyu Chen, Dong Lu, Ziyi Xiao, Shensuo Li, Wen Zhang, Xin Luan, Weidong Zhang, Guangyong Zheng

{"title":"Comprehensive applications of the artificial intelligence technology in new drug research and development.","authors":"Hongyu Chen, Dong Lu, Ziyi Xiao, Shensuo Li, Wen Zhang, Xin Luan, Weidong Zhang, Guangyong Zheng","doi":"10.1007/s13755-024-00300-y","DOIUrl":"10.1007/s13755-024-00300-y","url":null,"abstract":"Purpose: Target-based strategy is a prevalent means of drug research and development (R&D), since targets provide effector molecules of drug action and offer the foundation of pharmacological investigation. Recently, the artificial intelligence (AI) technology has been utilized in various stages of drug R&D, where AI-assisted experimental methods show higher efficiency than sole experimental ones. It is a critical need to give a comprehensive review of AI applications in drug R &D for biopharmaceutical field.Methods: Relevant literatures about AI-assisted drug R&D were collected from the public databases (Including Google Scholar, Web of Science, PubMed, IEEE Xplore Digital Library, Springer, and ScienceDirect) through a keyword searching strategy with the following terms [(\"Artificial Intelligence\" OR \"Knowledge Graph\" OR \"Machine Learning\") AND (\"Drug Target Identification\" OR \"New Drug Development\")].Results: In this review, we first introduced common strategies and novel trends of drug R&D, followed by characteristic description of AI algorithms widely used in drug R&D. Subsequently, we depicted detailed applications of AI algorithms in target identification, lead compound identification and optimization, drug repurposing, and drug analytical platform construction. Finally, we discussed the challenges and prospects of AI-assisted methods for drug discovery.Conclusion: Collectively, this review provides comprehensive overview of AI applications in drug R&D and presents future perspectives for biopharmaceutical field, which may promote the development of drug industry.","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"12 1","pages":"41"},"PeriodicalIF":3.4,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11310389/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141917743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel multi-modal model to assist the diagnosis of autism spectrum disorder using eye-tracking data. 利用眼动数据辅助诊断自闭症谱系障碍的新型多模态模型。

IF 3.4 3区医学

Health Information Science and Systems Pub Date : 2024-08-03 eCollection Date: 2024-12-01 DOI: 10.1007/s13755-024-00299-2

Brahim Benabderrahmane, Mohamed Gharzouli, Amira Benlecheb

{"title":"A novel multi-modal model to assist the diagnosis of autism spectrum disorder using eye-tracking data.","authors":"Brahim Benabderrahmane, Mohamed Gharzouli, Amira Benlecheb","doi":"10.1007/s13755-024-00299-2","DOIUrl":"10.1007/s13755-024-00299-2","url":null,"abstract":"Background and objective: Timely and accurate detection of Autism Spectrum Disorder (ASD) is essential for early intervention and improved patient outcomes. This study aims to harness the power of machine learning (ML) techniques to improve ASD detection by incorporating temporal eye-tracking data. We developed a novel ML model to leverage eye scan paths, sequences of distances of eye movement, and a sequence of fixation durations, enhancing the temporal aspect of the analysis for more effective ASD identification.Methods: We utilized a dataset of eye-tracking data without augmentation to train our ML model, which consists of a CNN-GRU-ANN architecture. The model was trained using gaze maps, the sequences of distances between eye fixations, and durations of fixations and saccades. Additionally, we employed a validation dataset to assess the model's performance and compare it with other works.Results: Our ML model demonstrated superior performance in ASD detection compared to the VGG-16 model. By incorporating temporal information from eye-tracking data, our model achieved higher accuracy, precision, and recall. The novel addition of sequence-based features allowed our model to effectively distinguish between ASD and typically developing individuals, achieving an impressive precision value of 93.10% on the validation dataset.Conclusion: This study presents an ML-based approach to ASD detection by utilizing machine learning techniques and incorporating temporal eye-tracking data. Our findings highlight the potential of temporal analysis for improved ASD detection and provide a promising direction for further advancements in the field of eye-tracking-based diagnosis and intervention for neurodevelopmental disorders.","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"12 1","pages":"40"},"PeriodicalIF":3.4,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11297859/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141894583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Linguistic summarization of visual attention and developmental functioning of young children with autism spectrum disorder. 自闭症谱系障碍幼儿视觉注意力和发育功能的语言总结。

IF 4.7 3区医学

Health Information Science and Systems Pub Date : 2024-07-16 eCollection Date: 2024-12-01 DOI: 10.1007/s13755-024-00297-4

Demet Öztürk, Sena Aydoğan, İbrahim Kök, Işık Akın Bülbül, Selda Özdemir, Suat Özdemir, Diyar Akay

{"title":"Linguistic summarization of visual attention and developmental functioning of young children with autism spectrum disorder.","authors":"Demet Öztürk, Sena Aydoğan, İbrahim Kök, Işık Akın Bülbül, Selda Özdemir, Suat Özdemir, Diyar Akay","doi":"10.1007/s13755-024-00297-4","DOIUrl":"10.1007/s13755-024-00297-4","url":null,"abstract":"Diagnosing autism spectrum disorder (ASD) in children poses significant challenges due to its complex nature and impact on social communication development. While numerous data analytics techniques have been proposed for ASD evaluation, the process remains time-consuming and lacks clarity. Eye tracking (ET) data has emerged as a valuable resource for ASD risk assessment, yet existing literature predominantly focuses on predictive methods rather than descriptive techniques that offer human-friendly insights. Interpretation of ET data and Bayley scales, a widely used assessment tool, is challenging for ASD assessment of children. It should be understood clearly to perform better analytic tasks on ASD screening. Therefore, this study addresses this gap by employing linguistic summarization techniques to generate easily understandable summaries from raw ET data and Bayley scales. By integrating ET data and Bayley scores, the study aims to improve the identification of children with ASD from typically developing children (TD). Notably, this research represents one of the pioneering efforts to linguistically summarize ET data alongside Bayley scales, presenting comparative results between children with ASD and TD. Through linguistic summarization, this study facilitates the creation of simple, natural language statements, offering a first and unique approach to enhance ASD screening and contribute to our understanding of neurodevelopmental disorders.","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"12 1","pages":"39"},"PeriodicalIF":4.7,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11252111/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141634953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving laryngeal cancer detection using chaotic metaheuristics integration with squeeze-and-excitation resnet model. 利用混沌元启发法与挤压-激发重网模型的整合改进喉癌检测。

IF 3.4 3区医学

Health Information Science and Systems Pub Date : 2024-07-12 eCollection Date: 2024-12-01 DOI: 10.1007/s13755-024-00296-5

Sana Alazwari, Mashael Maashi, Jamal Alsamri, Mohammad Alamgeer, Shouki A Ebad, Saud S Alotaibi, Marwa Obayya, Samah Al Zanin

{"title":"Improving laryngeal cancer detection using chaotic metaheuristics integration with squeeze-and-excitation resnet model.","authors":"Sana Alazwari, Mashael Maashi, Jamal Alsamri, Mohammad Alamgeer, Shouki A Ebad, Saud S Alotaibi, Marwa Obayya, Samah Al Zanin","doi":"10.1007/s13755-024-00296-5","DOIUrl":"10.1007/s13755-024-00296-5","url":null,"abstract":"Laryngeal cancer (LC) represents a substantial world health problem, with diminished survival rates attributed to late-stage diagnoses. Correct treatment for LC is complex, particularly in the final stages. This kind of cancer is a complex malignancy inside the head and neck region of patients. Recently, researchers serving medical consultants to recognize LC efficiently develop different analysis methods and tools. However, these existing tools and techniques have various problems regarding performance constraints, like lesser accuracy in detecting LC at the early stages, additional computational complexity, and colossal time utilization in patient screening. Deep learning (DL) approaches have been established that are effective in the recognition of LC. Therefore, this study develops an efficient LC Detection using the Chaotic Metaheuristics Integration with the DL (LCD-CMDL) technique. The LCD-CMDL technique mainly focuses on detecting and classifying LC utilizing throat region images. In the LCD-CMDL technique, the contrast enhancement process uses the CLAHE approach. For feature extraction, the LCD-CMDL technique applies the Squeeze-and-Excitation ResNet (SE-ResNet) model to learn the complex and intrinsic features from the image preprocessing. Moreover, the hyperparameter tuning of the SE-ResNet approach is performed using a chaotic adaptive sparrow search algorithm (CSSA). Finally, the extreme learning machine (ELM) model was applied to detect and classify the LC. The performance evaluation of the LCD-CMDL approach occurs utilizing a benchmark throat region image database. The experimental values implied the superior performance of the LCD-CMDL approach over recent state-of-the-art approaches.","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"12 1","pages":"38"},"PeriodicalIF":3.4,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11239646/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141617384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A multi-source heterogeneous medical data enhancement framework based on lakehouse. 基于 Lakehouse 的多源异构医疗数据增强框架。

IF 3.4 3区医学

Health Information Science and Systems Pub Date : 2024-07-05 eCollection Date: 2024-12-01 DOI: 10.1007/s13755-024-00295-6

Ming Sheng, Shuliang Wang, Yong Zhang, Rui Hao, Ye Liang, Yi Luo, Wenhan Yang, Jincheng Wang, Yinan Li, Wenkui Zheng, Wenyao Li

{"title":"A multi-source heterogeneous medical data enhancement framework based on lakehouse.","authors":"Ming Sheng, Shuliang Wang, Yong Zhang, Rui Hao, Ye Liang, Yi Luo, Wenhan Yang, Jincheng Wang, Yinan Li, Wenkui Zheng, Wenyao Li","doi":"10.1007/s13755-024-00295-6","DOIUrl":"10.1007/s13755-024-00295-6","url":null,"abstract":"Obtaining high-quality data sets from raw data is a key step before data exploration and analysis. Nowadays, in the medical domain, a large amount of data is in need of quality improvement before being used to analyze the health condition of patients. There have been many researches in data extraction, data cleaning and data imputation, respectively. However, there are seldom frameworks integrating with these three techniques, making the dataset suffer in accuracy, consistency and integrity. In this paper, a multi-source heterogeneous data enhancement framework based on a lakehouse MHDP is proposed, which includes three steps of data extraction, data cleaning and data imputation. In the data extraction step, a data fusion technique is offered to handle multi-modal and multi-source heterogeneous data. In the data cleaning step, we propose HoloCleanX, which provides a convenient interactive procedure. In the data imputation step, multiple imputation (MI) and the SOTA algorithm SAITS, are applied for different situations. We evaluate our framework via three tasks: clustering, classification and strategy prediction. The experimental results prove the effectiveness of our data enhancement framework.","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"12 1","pages":"37"},"PeriodicalIF":3.4,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11226589/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141555685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A transfer learning enabled approach for ocular disease detection and classification. 眼科疾病检测和分类的迁移学习方法。

IF 3.4 3区医学

Health Information Science and Systems Pub Date : 2024-06-11 eCollection Date: 2024-12-01 DOI: 10.1007/s13755-024-00293-8

Mahmood Ul Hassan, Amin A Al-Awady, Naeem Ahmed, Muhammad Saeed, Jarallah Alqahtani, Ali Mousa Mohamed Alahmari, Muhammad Wasim Javed

{"title":"A transfer learning enabled approach for ocular disease detection and classification.","authors":"Mahmood Ul Hassan, Amin A Al-Awady, Naeem Ahmed, Muhammad Saeed, Jarallah Alqahtani, Ali Mousa Mohamed Alahmari, Muhammad Wasim Javed","doi":"10.1007/s13755-024-00293-8","DOIUrl":"10.1007/s13755-024-00293-8","url":null,"abstract":"Ocular diseases pose significant challenges in timely diagnosis and effective treatment. Deep learning has emerged as a promising technique in medical image analysis, offering potential solutions for accurately detecting and classifying ocular diseases. In this research, we propose Ocular Net, a novel deep learning model for detecting and classifying ocular diseases, including Cataracts, Diabetic, Uveitis, and Glaucoma, using a large dataset of ocular images. The study utilized an image dataset comprising 6200 images of both eyes of patients. Specifically, 70% of these images (4000 images) were allocated for model training, while the remaining 30% (2200 images) were designated for testing purposes. The dataset contains images of five categories that include four diseases, and one normal category. The proposed model uses transfer learning, average pooling layers, Clipped Relu, Leaky Relu and various other layers to accurately detect the ocular diseases from images. Our approach involves training a novel Ocular Net model on diverse ocular images and evaluating its accuracy and performance metrics for disease detection. We also employ data augmentation techniques to improve model performance and mitigate overfitting. The proposed model is tested on different training and testing ratios with varied parameters. Additionally, we compare the performance of the Ocular Net with previous methods based on various evaluation parameters, assessing its potential for enhancing the accuracy and efficiency of ocular disease diagnosis. The results demonstrate that Ocular Net achieves 98.89% accuracy and 0.12% loss value in detecting and classifying ocular diseases by outperforming existing methods.","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"12 1","pages":"36"},"PeriodicalIF":3.4,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11164840/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141311973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development of a recommendation system and data analysis in personalized medicine: an approach towards healthy vascular ageing. 开发个性化医疗推荐系统和数据分析：实现血管健康老龄化的方法。

IF 3.4 3区医学

Health Information Science and Systems Pub Date : 2024-05-03 eCollection Date: 2024-12-01 DOI: 10.1007/s13755-024-00292-9

Arturo Martinez-Rodrigo, Jose Carlos Castillo, Alicia Saz-Lara, Iris Otero-Luis, Iván Cavero-Redondo

{"title":"Development of a recommendation system and data analysis in personalized medicine: an approach towards healthy vascular ageing.","authors":"Arturo Martinez-Rodrigo, Jose Carlos Castillo, Alicia Saz-Lara, Iris Otero-Luis, Iván Cavero-Redondo","doi":"10.1007/s13755-024-00292-9","DOIUrl":"10.1007/s13755-024-00292-9","url":null,"abstract":"Purpose: Understanding early vascular ageing has become crucial for preventing adverse cardiovascular events. To this respect, recent AI-based risk clustering models offer early detection strategies focused on healthy populations, yet their complexity limits clinical use. This work introduces a novel recommendation system embedded in a web app to assess and mitigate early vascular ageing risk, leading patients towards improved cardiovascular health.Methods: This system employs a methodology that calculates distances within multidimensional spaces and integrates cost functions to obtain personalized optimisation of recommendations. It also incorporates a classification system for determining the intensity levels of the clinical interventions.Results: The recommendation system showed high efficiency in identifying and visualizing individuals at high risk of early vascular ageing among healthy patients. Additionally, the system corroborated its consistency and reliability in generating personalized recommendations among different levels of granularity, emphasizing its focus on moderate or low-intensity recommendations, which could improve patient adherence to the intervention.Conclusion: This tool might significantly aid healthcare professionals in their daily analysis, improving the prevention and management of cardiovascular diseases.","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"12 1","pages":"34"},"PeriodicalIF":3.4,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11068708/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140865388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatial-attention ConvMixer architecture for classification and detection of gastrointestinal diseases using the Kvasir dataset. 利用 Kvasir 数据集对胃肠道疾病进行分类和检测的空间注意力 ConvMixer 架构。

IF 4.7 3区医学

Health Information Science and Systems Pub Date : 2024-04-28 eCollection Date: 2024-12-01 DOI: 10.1007/s13755-024-00290-x

Ayşe Ayyüce Demirbaş, Hüseyin Üzen, Hüseyin Fırat

{"title":"Spatial-attention ConvMixer architecture for classification and detection of gastrointestinal diseases using the Kvasir dataset.","authors":"Ayşe Ayyüce Demirbaş, Hüseyin Üzen, Hüseyin Fırat","doi":"10.1007/s13755-024-00290-x","DOIUrl":"10.1007/s13755-024-00290-x","url":null,"abstract":"Gastrointestinal (GI) disorders, encompassing conditions like cancer and Crohn's disease, pose a significant threat to public health. Endoscopic examinations have become crucial for diagnosing and treating these disorders efficiently. However, the subjective nature of manual evaluations by gastroenterologists can lead to potential errors in disease classification. In addition, the difficulty of diagnosing diseased tissues in GI and the high similarity between classes made the subject a difficult area. Automated classification systems that use artificial intelligence to solve these problems have gained traction. Automatic detection of diseases in medical images greatly benefits in the diagnosis of diseases and reduces the time of disease detection. In this study, we suggested a new architecture to enable research on computer-assisted diagnosis and automated disease detection in GI diseases. This architecture, called Spatial-Attention ConvMixer (SAC), further developed the patch extraction technique used as the basis of the ConvMixer architecture with a spatial attention mechanism (SAM). The SAM enables the network to concentrate selectively on the most informative areas, assigning importance to each spatial location within the feature maps. We employ the Kvasir dataset to assess the accuracy of classifying GI illnesses using the SAC architecture. We compare our architecture's results with Vanilla ViT, Swin Transformer, ConvMixer, MLPMixer, ResNet50, and SqueezeNet models. Our SAC method gets 93.37% accuracy, while the other architectures get respectively 79.52%, 74.52%, 92.48%, 63.04%, 87.44%, and 85.59%. The proposed spatial attention block improves the accuracy of the ConvMixer architecture on the Kvasir, outperforming the state-of-the-art methods with an accuracy rate of 93.37%.","PeriodicalId":46312,"journal":{"name":"Health Information Science and Systems","volume":"12 1","pages":"32"},"PeriodicalIF":4.7,"publicationDate":"2024-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11056348/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140872890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0