Applied Engineering and Technology最新文献

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Deep learning-based cervical lesion segmentation in colposcopic images 基于深度学习的阴道镜图像中的宫颈病变分割

Applied Engineering and Technology Pub Date : 2024-04-03 DOI: 10.31763/aet.v3i1.1345

Lalasa Mukku, Jyothi Thomas

{"title":"Deep learning-based cervical lesion segmentation in colposcopic images","authors":"Lalasa Mukku, Jyothi Thomas","doi":"10.31763/aet.v3i1.1345","DOIUrl":"https://doi.org/10.31763/aet.v3i1.1345","url":null,"abstract":"Artificial intelligence assisted cancer detection has changed the ream of diagnosis precision. This study aims to propose a segmentation network using artificial intelligence for accurately segmenting the cervix region and acetowhite lesions in cervigram images, addressing the shortage of skilled colposcopists and streamlining the training process. A computational approach is employed to develop and train a deep learning model specifically tailored for cervix region and acetowhite lesion segmentation in cervigram images. A dataset acquired in collaboration with KIDWAI memorial cancer research institute is used for building the model. Cervigram images are collected for training and validation, and a deep learning architecture is constructed and trained using annotated datasets. The segmentation network based on efficientnet architecture and atrous spatial pyramid pooling is designed to accurately identify and delineate the target regions, with performance evaluation conducted using precision, accuracy, recall, dice score, and specificity metrics. The proposed segmentation network achieves a precision of 0.7387±0.1541, accuracy of 0.9291, recall of 0.7912±0.1439, dice score of 0.7431±0.1506, and specificity of 0.9589±0.0131, indicating its reliability and robustness in segmenting cervix regions and acetowhite lesions in cervigram images. This research demonstrates the feasibility and effectiveness of using artificial intelligence-based computational models for cervix region and acetowhite lesion segmentation in cervigram images. It provides a foundation for further investigations into classifying cervix malignancy using AI techniques, potentially enhancing early detection and treatment of cervical cancer while addressing the shortage of skilled professionals in the field ","PeriodicalId":511141,"journal":{"name":"Applied Engineering and Technology","volume":"37 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140747046","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

Performance Analysis of Random Forest on Quartile Classification Journal 随机森林在四分位分类中的性能分析期刊

Applied Engineering and Technology Pub Date : 2024-03-22 DOI: 10.31763/aet.v3i1.1189

Cornaldo Beliarding Sucahyo, Fajriwati Qoyyum Rizqini, Ayyub Naufal, Hengky Yandratama, Jabar Ash Shiddiqy, Agung Bella Putra Utama, Nastiti Susetyo Fanany Putri, A. Wibawa

{"title":"Performance Analysis of Random Forest on Quartile Classification Journal","authors":"Cornaldo Beliarding Sucahyo, Fajriwati Qoyyum Rizqini, Ayyub Naufal, Hengky Yandratama, Jabar Ash Shiddiqy, Agung Bella Putra Utama, Nastiti Susetyo Fanany Putri, A. Wibawa","doi":"10.31763/aet.v3i1.1189","DOIUrl":"https://doi.org/10.31763/aet.v3i1.1189","url":null,"abstract":"Journals play a pivotal role in disseminating scientific knowledge, housing a multitude of valuable research articles. In this digital age, the evaluation of journals and their quality is essential. The SCImago Journal Rank (SJR) stands as one of the prominent platforms for ranking journals, categorizing them into five index classes: Q1, Q2, Q3, Q4, and NQ. Determining these index classes often relies on classification methodologies. This research, drawing inspiration from the Cross-Industry Standard Process for Data Mining (CRISP-DM), seeks to employ the Random Forest method to classify journals, thus contributing to the refinement of journal ranking processes. Random Forest stands out as a robust choice due to its remarkable ability to mitigate overfitting, a common challenge in machine learning classification tasks. In the context of approximating SJR index classes, Random Forest, when utilizing the Gini index, exhibits promise, albeit with an initial accuracy rate of 62.12%. The Gini index, an impurity measure, enables Random Forest to make informed decisions while classifying journals into their respective SJR index classes. However, it is worth noting that this accuracy rate represents a starting point, and further refinement and feature engineering may enhance the model's performance. This research underscores the significance of machine learning techniques in the domain of journal classification and journal-ranking systems. By harnessing the power of Random Forest, this study aims to facilitate more accurate and efficient categorization of journals, thereby aiding researchers, academics, and institutions in identifying and accessing high-quality scientific literature.","PeriodicalId":511141,"journal":{"name":"Applied Engineering and Technology","volume":" 49","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140212869","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