scholarly journals Restricted Boltzmann Machines for Classification of Hepatocellular Carcinoma

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
James A. Koziol ◽  
Eng M. Tan ◽  
Liping Dai ◽  
Pengfei Ren ◽  
Jian-Ying Zhang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cancer Detection ◽  
Classification Problem ◽  
Classification Problems ◽  
Operating Characteristics ◽  
Restricted Boltzmann Machines ◽  
Boltzmann Machines ◽  
Multiple Antigen ◽  
Detection And Diagnosis

Multiple antigen miniarrays can provide accurate tools for cancer detection and diagnosis. These miniarrays can be validated by examining their operating characteristics in classifying individuals as either cancer patients or normal (non-cancer) subjects. We describe the use of restricted Boltzmann machines for this classification problem, relative to diagnosis of hepatocellular carcinoma. In this setting, we find that its operating characteristics are similar to a logistic regression standard and suggest that restricted Boltzmann machines merit further consideration for classification problems.

scholarly journals Research on Complex Classification Algorithm of Breast Cancer Chip Based on SVM-RFE Gene Feature Screening

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Guobin Chen ◽  
Xianzhong Xie ◽  
Shijin Li
Keyword(s):  
Gene Expression ◽  
Classification Problem ◽  
Classification Algorithm ◽  
Gene Chip ◽  
Classification Problems ◽  
Gene Chips ◽  
Gene Expression Matrix ◽  
Performance Indexes ◽  
Characteristic Sequences

Screening and classification of characteristic genes is a complex classification problem, and the characteristic sequences of gene expression show high-dimensional characteristics. How to select an effective gene screening algorithm is the main problem to be solved by analyzing gene chips. The combination of KNN, SVM, and SVM-RFE is selected to screen complex classification problems, and a new method to solve complex classification problems is provided. In the process of gene chip pretreatment, LogFC and P value equivalents in the gene expression matrix are screened, and different gene features are screened, and then SVM-RFE algorithm is used to sort and screen genes. Firstly, the characteristics of gene chips are analyzed and the number between probes and genes is counted. Clustering analysis among each sample and PCA classification analysis of different samples are carried out. Secondly, the basic algorithms of SVM and KNN are tested, and the important indexes such as error rate and accuracy rate of the algorithms are tested to obtain the optimal parameters. Finally, the performance indexes of accuracy, precision, recall, and F1 of several complex classification algorithms are compared through the complex classification of SVM, KNN, KNN-PCA, SVM-PCA, SVM-RFE-SVM, and SVM-RFE-KNN at P=0. 01,0.05,0.001. SVM-RFE-SVM has the best classification effect and can be used as a gene chip classification algorithm to analyze the characteristics of genes.

scholarly journals Restricted Boltzmann Machines for Fundus Image Reconstruction and Classification of Hypertension Retinopathy

2021 ◽  
Vol 17 (2) ◽  
pp. 156-166
Author(s):  
Bambang Krismono Triwijoyo ◽  
Boy Subirosa Sabarguna ◽  
Widodo Budiharto ◽  
Edi Abdurachman
Keyword(s):  
Image Reconstruction ◽  
Fundus Image ◽  
Restricted Boltzmann Machines ◽  
Boltzmann Machines

scholarly journals Polish Metric Spaces: Their Classification and Isometry Groups

2001 ◽  
Vol 7 (3) ◽  
pp. 361-375 ◽  
Author(s):  
John D. Clemens ◽  
Su Gao ◽  
Alexander S. Kechris
Keyword(s):  
Metric Space ◽  
Metric Spaces ◽  
Classification Problem ◽  
Classification Problems ◽  
Isometry Groups ◽  
Polish Spaces ◽  
Basic Ideas ◽  
In The Beginning ◽  
Theory Of Complexity

§ 1. Introduction. In this communication we present some recent results on the classification of Polish metric spaces up to isometry and on the isometry groups of Polish metric spaces. A Polish metric space is a complete separable metric space (X, d).Our first goal is to determine the exact complexity of the classification problem of general Polish metric spaces up to isometry. This work was motivated by a paper of Vershik [1998], where he remarks (in the beginning of Section 2): “The classification of Polish spaces up to isometry is an enormous task. More precisely, this classification is not ‘smooth’ in the modern terminology.” Our Theorem 2.1 below quantifies precisely the enormity of this task.After doing this, we turn to special classes of Polish metric spaces and investigate the classification problems associated with them. Note that these classification problems are in principle no more complicated than the general one above. However, the determination of their exact complexity is not necessarily easier.The investigation of the classification problems naturally leads to some interesting results on the groups of isometries of Polish metric spaces. We shall also present these results below.The rest of this section is devoted to an introduction of some basic ideas of a theory of complexity for classification problems, which will help to put our results in perspective. Detailed expositions of this general theory can be found, e.g., in Hjorth [2000], Kechris [1999], [2001].

scholarly journals CO-ResNet: Optimized ResNet model for COVID-19 diagnosis from X-ray images

2021 ◽  
pp. 1-15
Author(s):  
Subrato Bharati ◽  
Prajoy Podder ◽  
M. Rubaiyat Hossain Mondal ◽  
V.B. Surya Prasath
Keyword(s):  
Classification Problem ◽  
Operating Characteristics ◽  
X Ray ◽  
Level Data ◽  
Receiver Operating Characteristics Curve ◽  
Multi Level ◽  
Novel Coronavirus ◽  
Normal Lungs ◽  
Healthy Lung

This paper focuses on the application of deep learning (DL) based model in the analysis of novel coronavirus disease (COVID-19) from X-ray images. The novelty of this work is in the development of a new DL algorithm termed as optimized residual network (CO-ResNet) for COVID-19. The proposed CO-ResNet is developed by applying hyperparameter tuning to the conventional ResNet 101. CO-ResNet is applied to a novel dataset of 5,935 X-ray images retrieved from two publicly available datasets. By utilizing resizing, augmentation and normalization and testing different epochs our CO-ResNet was optimized for detecting COVID-19 versus pneumonia with normal healthy lung controls. Different evaluation metrics such as the classification accuracy, F1 score, recall, precision, area under the receiver operating characteristics curve (AUC) are used. Our proposed CO-ResNet obtains consistently best performance in the multi-level data classification problem, including health lung, pneumonia affected lung and COVID-19 affected lung samples. In the experimental evaluation, the detection rate accuracy in discerning COVID-19 is 98.74%, and for healthy normal lungs, pneumonia affected lungs are 92.08% and 91.32% respectively for our CO-ResNet with ResNet101 backbone. Further, our model obtained accuracy values of 83.68% and 82% for healthy normal lungs and pneumonia affected lungs with ResNet152 backbone. Experimental results indicate the potential usage of our new DL driven model for classification of COVID-19 and pneumonia.

On the application of the system of machines modular block type in the advanced technology logging activities

2016 ◽  
Author(s):  
С.А. Демидов
Keyword(s):  
Advanced Technology ◽  
Modular System ◽  
Process Equipment ◽  
Block Type ◽  
Work Efficiency ◽  
Operating Characteristics ◽  
Promising Direction ◽  
Hydrostatic Transmission ◽  
Machine Module

Рассматриваются возможности применения системы машин модульно-блочного типа в перспективных технологиях лесосечных работ, а также проблемы машинного парка лесозаготовительных предприятий России. Цель исследования - изучение структуры и особенностей эксплуатации системы лесных машин модульно блочного типа в современных условиях лесозаготовок с перспективой применения на ближайшее будущее. Для улучшения технологии проведения лесосечных работ и повышения экономической эффективности лесопромышленного комплекса предлагается провести ряд важных технических и технологических изменений. Одним из решений проблемы по улучшению эффективности работы является разработка и внедрение комплекса лесных машин, основанного на принципе эксплуатационной модульности. Это будет гарантировать технологическую гибкость производства, предоставит высокую производительность и обеспечит совместимость с окружающей средой. Приведен принцип устройства машин модульно-блочного типа с разделением функций между транспортными и технологическими модулями. Представлены графически классификация модулей по их назначению и концепция компоновки системы машин модульно-блочного типа, а также главный модуль (энергетический) и несколько технологических модулей, способных выполнять различные технологические операции в зависимости от условий и технологии производства. Как показал анализ рынка, наиболее перспективным направлением по улучшению механизации лесного парка машин является создание комплекса модульно-блочных машин на базе колесного трактора, оснащенных гидрообъемными передачами. Это делает конструкцию машины более гибкой и мобильной. Принцип формирования и работы модульной системы машин с многофункциональным технологическим оборудованием рассматривается в качестве перспективного направления по улучшению лесозаготовительного процесса. The article deals with the prospects for the use of machines modular block type in the advanced technology logging activities, as well as the machinery problems of logging enterprises in Russia. The research objective. The study of the structure and operating characteristics of a system of forest modular block type machines in current conditions with the prospect of their application in the nearest future. It is necessary that a number of important technical and technological changes should be made to improve the technology of logging operations and increase the economic efficiency of timber industry complex. One of the solutions to improve work efficiency is the development and introduction of forest machines based on the principle of operational modularity. It will ensure the flexibility of the production process, provide high work efficiency and, what counts, will be environmentally friendly. The article gives the description of the principle of the machine module block type, as well as the functions and how they are divided between the two modules. There are two pictures in the article. The first picture gives the classification of modules according to their application. The second picture shows the principle of arrangement of modular machine-block type. Both the main energetic module and some technological modules capable of performing processing steps depending on the conditions and production technology are presented. According to the market analysis that shows that the most promising direction to improve the mechanization of forest machinery is to create a complex modular block machines on the base of a wheeled tractor equipped with hydrostatic transmission. It will make the machine design more flexible and mobile. The principle of formation and operation of the modular system with multi-function machines process equipment is considered as a promising direction for improvement of the process of logging.

scholarly journals Radiofrequency ablation versus repeat hepatectomy in the treatment of recurrent hepatocellular carcinoma in subcapsular location: a retrospective cohort study

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Fuqun Wei ◽  
Qizhen Huang ◽  
Yang Zhou ◽  
Liuping Luo ◽  
Yongyi Zeng
Keyword(s):  
Hepatocellular Carcinoma ◽  
Radiofrequency Ablation ◽  
Repeat Hepatectomy ◽  
Free Survival ◽  
Recurrent Hepatocellular Carcinoma ◽  
Primary Endpoints ◽  
Grade 3 ◽  
Optimal Treatment Strategy ◽  
Hepatectomy Group

Abstract Background Repeat hepatectomy and radiofrequency ablation (RFA) are widely used to treat early recurrent hepatocellular carcinoma (RHCC) located in the subcapsular region, but the optimal treatment strategy remains to be controversial. Methods A total of 126 RHCC patients in the subcapsular location after initial radical hepatectomy were included in this study between Dec 2014 and Jan 2018. These patients were divided into the RFA group (46 cases) and the repeat hepatectomy group (80 cases). The primary endpoints include repeat recurrence-free survival (rRFS) and overall survival (OS), and the secondary endpoint was complications. The propensity-score matching (PSM) was conducted to minimize the bias. Complications were evaluated using the Clavien-Dindo classification, and severe complications were defined as classification of complications of ≥grade 3. Results There were no significant differences in the incidence of severe complications were observed between RFA group and repeat hepatectomy group in rRFS and OS both before (1-, 2-, and 3-year rRFS rates were 65.2%, 47.5%, and 33.3% vs 72.5%, 51.2%, and 39.2%, respectively, P = 0.48; 1-, 2-, and 3-year OS rates were 93.5%, 80.2%, and 67.9% vs 93.7%, 75.8%, and 64.2%, respectively, P = 0.92) and after PSM (1-, 2-, and 3-year rRFS rates were 68.6%, 51.0%, and 34.0% vs 71.4%, 42.9%, and 32.3%, respectively, P = 0.78; 1-, 2-, and 3-year OS rates were 94.3%, 82.9%, and 71.4% vs 88.6%, 73.8%, and 59.0%, respectively, P = 0.36). Moreover, no significant differences in the incidence of severe complications were observed between the RFA group and repeat hepatectomy group. Conclusion Both repeat hepatectomy and RFA are shown to be effective and safe for the treatment of RHCC located in the subcapsular region.

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