Bio-Algorithms and Med-Systems最新文献_第6页

Image processing algorithms in the assessment of grain damage degree 图像处理算法在晶粒损伤程度评估中的应用

IF 1.2

Bio-Algorithms and Med-Systems Pub Date : 2021-10-12 DOI: 10.1515/bams-2021-0063

Wojciech Maliga, W. Dudziński, M. Łabowska, J. Detyna, Marcin Łopusiewicz, H. Bujak

{"title":"Image processing algorithms in the assessment of grain damage degree","authors":"Wojciech Maliga, W. Dudziński, M. Łabowska, J. Detyna, Marcin Łopusiewicz, H. Bujak","doi":"10.1515/bams-2021-0063","DOIUrl":"https://doi.org/10.1515/bams-2021-0063","url":null,"abstract":"Abstract Objectives The paper presents preliminary results on the assessment of algorithms used in image processing of the grain damage degree. The purpose of the work is developing a tool allowing to analyse sample cross-sections of rye germs. Methods The analysis of the grain cross-sections was carried out on the basis of a series their photos taken at equal time intervals at a set depth. The cross-sections will be used to create additional virtual cross-sections allowing to analyse the whole sample volume. The ultimate plan is to generate two cross-sections perpendicular to each other. Based on volumetric data read from the sample section, a three-dimensional model of an object will be generated. Results The analysis of model surface will allowed us to detect possible grain damage. The developed method of preparing the research material and the proprietary application allowed for the identification of internal defects in the biological material (cereal grains). Conclusions The presented methodology may be used in the agri-food industry in the future. However, much research remains to be done. These works should primarily aim at significantly reducing the time-consuming nature of individual stages, as well as improving the quality of the reconstructed image.","PeriodicalId":42620,"journal":{"name":"Bio-Algorithms and Med-Systems","volume":"18 1","pages":"17 - 28"},"PeriodicalIF":1.2,"publicationDate":"2021-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48651390","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: 1

Ovarian cancer diagnosis using pretrained mask CNN-based segmentation with VGG-19 architecture 基于VGG-19架构的预训练掩模cnn分割卵巢癌诊断

IF 1.2

Bio-Algorithms and Med-Systems Pub Date : 2021-09-29 DOI: 10.1515/bams-2021-0098

K. Senthil, Vidyaathulasiraman

引用次数: 2

Brain–computer interface for electric wheelchair based on alpha waves of EEG signal 基于脑电信号α波的电动轮椅脑机接口

IF 1.2

Bio-Algorithms and Med-Systems Pub Date : 2021-09-01 DOI: 10.1515/bams-2021-0095

Kacper Banach, Mateusz Małecki, M. Rosół, A. Broniec

{"title":"Brain–computer interface for electric wheelchair based on alpha waves of EEG signal","authors":"Kacper Banach, Mateusz Małecki, M. Rosół, A. Broniec","doi":"10.1515/bams-2021-0095","DOIUrl":"https://doi.org/10.1515/bams-2021-0095","url":null,"abstract":"Abstract Objectives Helping patients suffering from serious neurological diseases that lead to hindering the independent movement is of high social importance and an interdisciplinary challenge for engineers. Brain–computer interface (BCI) interfaces based on the electroencephalography (EEG) signal are not easy to use as they require time consuming multiple electrodes montage. We aimed to contribute in bringing BCI systems outside the laboratories so that it could be more accessible to patients, by designing a wheelchair fully controlled by an algorithm using alpha waves and only a few electrodes. Methods The set of eight binary words are designed, that allow to move forward, backward, turn right and left, rotate 45° as well as to increase and decrease the speed of the wheelchair. Our project includes: development of a mobile application which is used as a graphical user interface, real-time signal processing of the EEG signal, development of electric wheelchair engines control system and mechanical construction. Results The average sensitivity, without training, was 79.58% and specificity 97.08%, on persons who had no previous contact with BCI. Conclusions The proposed system can be helpful for people suffering from incurable diseases that make them closed in their bodies and for whom communication with the surrounding world is almost impossible.","PeriodicalId":42620,"journal":{"name":"Bio-Algorithms and Med-Systems","volume":"17 1","pages":"165 - 172"},"PeriodicalIF":1.2,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47731778","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: 5

Blue-emitting polystyrene scintillators for plastic scintillation dosimetry 塑料闪烁剂量测定用蓝色发光聚苯乙烯闪烁体

IF 1.2

Bio-Algorithms and Med-Systems Pub Date : 2021-09-01 DOI: 10.1515/bams-2021-0088

Ł. Kapłon, G. Moskal

引用次数: 11

Regularization based discriminative feature pattern selection for the classification of Parkinson cases using machine learning 基于正则化的判别特征模式选择用于机器学习的帕金森病例分类

IF 1.2

Bio-Algorithms and Med-Systems Pub Date : 2021-08-19 DOI: 10.1515/bams-2021-0064

Kamalakannan Kaliyan, Anand Ganesan

{"title":"Regularization based discriminative feature pattern selection for the classification of Parkinson cases using machine learning","authors":"Kamalakannan Kaliyan, Anand Ganesan","doi":"10.1515/bams-2021-0064","DOIUrl":"https://doi.org/10.1515/bams-2021-0064","url":null,"abstract":"Abstract Objectives This paper focuses on developing a regularization-based feature selection approach to select the most effective attributes from the Parkinson’s speech dataset. Parkinson’s disease is a medical condition that progresses as the dopamine-producing nerve cells are affected. Early diagnosis often reduces the effect on the individuals, minimizes the advancement over time. In recent times, intelligent computational models are used in many complex cases to diagnose a clinical condition with high precision. These models are intended to find meaningful representation from the data to diagnose the disease. Machine learning acts as a tool, gears up the model learning process through a mathematical baseline. But, not in all cases, machine learning will be demanded to perform optimally. It comes with a few constraints, mainly the representation of the data. The learning models expect a clean, noise-free input, which in-turns produces better discriminative patterns over different categories of classes. Methods The proposed model identified five candidate features as predictors. This feature subset is trained with different varieties of supervised classifiers to trace out the best-performing model. Results The results are validated through accuracy, precision, recall, and receiver’s operational characteristic curves. The proposed regularization- based feature selection model outperformed the benchmark algorithms by attaining 100% accuracy on most of the classifiers, other than linear discriminant analysis (99.90%) and naïve Bayes (99.51%). Conclusions This paper exhibits the need for intelligent models to analyze complex data patterns to assist medical practitioners in better disease diagnosis. The results exhibit that the regularization methods find the best features based on their importance score, which improved the model performance over other feature selection methods.","PeriodicalId":42620,"journal":{"name":"Bio-Algorithms and Med-Systems","volume":"17 1","pages":"181 - 189"},"PeriodicalIF":1.2,"publicationDate":"2021-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43747534","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: 1

Machine learning-based approach for segmentation of intervertebral disc degeneration from lumbar section of spine using MRI images 基于机器学习的腰椎椎间盘退变MRI图像分割方法

IF 1.2

Bio-Algorithms and Med-Systems Pub Date : 2021-08-12 DOI: 10.1515/bams-2020-0047

Jayashri Shinde, Y. Joshi, R. Manthalkar, Joshi

{"title":"Machine learning-based approach for segmentation of intervertebral disc degeneration from lumbar section of spine using MRI images","authors":"Jayashri Shinde, Y. Joshi, R. Manthalkar, Joshi","doi":"10.1515/bams-2020-0047","DOIUrl":"https://doi.org/10.1515/bams-2020-0047","url":null,"abstract":"Abstract Objectives Intervertebral disc segmentation is one of the methods to diagnose spinal disease through the degeneration in asymptomatic and symptomatic patients. Even though numerous intervertebral disc segmentation techniques are available, classifying the grades in the intervertebral disc is a hectic challenge in the existing disc segmentation methods. Thus, an effective Whale Spine-Generative Adversarial Network (WSpine-GAN) method is proposed to segment the intervertebral disc for effective grade classification. Methods The proposed WSpine-GAN method effectively performs the disc segmentation, wherein the weights of Spine-GAN are optimally tuned using Whale Optimization Algorithm (WOA). Then, the refined disc features, such as pixel-based features and the connectivity features are extracted. Finally, the K-Nearest Neighbor (KNN) classifier based on the pfirrmann’s grading system performs the grade classification. Results The implementation of the grade classification strategy based on the proposed WSpine-GAN and KNN is performed using the real-time database, and the performance based on the metrics yielded the accuracy, true positive rate (TPR), and false positive rate (FPR) values of 97.778, 97.83, and 0.586% for the training percentage and 92.382, 90.580, and 1.972% for the K-fold value. Conclusions The proposed WSpine-GAN method effectively performs the disc segmentation by integrating the Spine-GANmethod and WOA. Here, the spinal cord images are segmented using the proposed WSpine-GAN method by tuning the weights optimally to enhance the performance of the disc segmentation.","PeriodicalId":42620,"journal":{"name":"Bio-Algorithms and Med-Systems","volume":"0 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2021-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42940821","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

Modeling the C. elegans germline stem cell genetic network using automated reasoning 利用自动推理对秀丽隐杆线虫生殖系干细胞遗传网络进行建模

IF 1.2

Bio-Algorithms and Med-Systems Pub Date : 2021-08-08 DOI: 10.1101/2021.08.08.455525

A. Amar, E. Hubbard, H. Kugler

{"title":"Modeling the C. elegans germline stem cell genetic network using automated reasoning","authors":"A. Amar, E. Hubbard, H. Kugler","doi":"10.1101/2021.08.08.455525","DOIUrl":"https://doi.org/10.1101/2021.08.08.455525","url":null,"abstract":"Computational methods and tools are a powerful complementary approach to experimental work for studying regulatory interactions in living cells and systems. We demonstrate the use of formal reasoning methods as applied to the Caenorhabditis elegans germ line, which is an accessible model system for stem cell research. The dynamics of the underlying genetic networks and their potential regulatory interactions are key for understanding mechanisms that control cellular decision-making between stem cells and differentiation. We model the “stem cell fate” versus entry into the “meiotic development” pathway decision circuit in the young adult germ line based on an extensive study of published experimental data and known/hypothesized genetic interactions. We apply a formal reasoning framework to derive predictive networks for control of differentiation. Using this approach we simultaneously specify many possible scenarios and experiments together with potential genetic interactions, and synthesize genetic networks consistent with all encoded experimental observations. In silico analysis of knock-down and overexpression experiments within our model recapitulate published phenotypes of mutant animals and can be applied to make predictions on cellular decision-making. This work lays a foundation for developing realistic whole tissue models of the C. elegans germ line where each cell in the model will execute a synthesized genetic network.","PeriodicalId":42620,"journal":{"name":"Bio-Algorithms and Med-Systems","volume":"6 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2021-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81559848","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: 1

An integrated review on machine learning approaches for heart disease prediction: Direction towards future research gaps 心脏疾病预测的机器学习方法综述:未来研究差距的方向

IF 1.2

Bio-Algorithms and Med-Systems Pub Date : 2021-07-08 DOI: 10.1515/bams-2020-0069

Fathima. A. Vellameeran, T. Brindha

引用次数: 1

Frontmatter

IF 1.2

Bio-Algorithms and Med-Systems Pub Date : 2021-06-01 DOI: 10.1515/bams-2021-frontmatter2