Wyniki wyszukiwania - Biblioteka Nauki

1

A deep attention network via high-resolution representation for liver and liver tumor segmentation

100%

Li Y. , Zou B. , Liu Q.

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2021

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tom Vol. 41, no. 4

1518--1532

EN

The segmentation of liver and liver tumor is an essential step for computer-aided liver disease diagnosis, treatment and prognosis. Although deep convolutional neural networks have contributed to liver and tumor segmentation, their architectures can not maintain spatial details and long-range context information. Besides, the fixed receptive fields of these networks limit the segmentation performance of livers and tumors with variant sizes and shapes. To address above problems, we propose a deep attention neural network which contains high-resolution branch and multi-scale features aggregation for cascaded liver and tumor segmentation from CT images. To be specific, the high-resolution branch can maintain the resolution of the input image and thus preserves the spatial details. The multi-scale features exchange and fusion enable the receptive fields of the network to adapt to liver and tumor with variant shapes and sizes. The appended attention module evaluates the similarities between every two pixels to model the long-range dependence and context information so that the network can segment liver and tumor areas located in distant regions. Experimental results on the LiTS and the 3D-IRCADb datasets demonstrate that our method can generate satisfying performance.

2

Optimal biomarker selection using adaptive Social Ski-Driver optimization for liver cancer detection

84%

Gunasekhar P. , Vijayalakshmi S.

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tom Vol. 40, no. 4

1611--1625

EN

Most essential biomolecule found in the human body is a biomarker; with these biomarkers, the abnormal biological processes and disease states of each patient can be accurately determined. Nowadays, the biomarker applications are frequently applied during clinical trials to identify cancer patients. In this method, the major significance of miRNA biomarkers during liver cancer detection is analysed. For such analysis, a deep learning technique is introduced along with optimization algorithms. Six different filter-based approaches are considered for feature selection they are Chi-Squared (Chi2), Information Gain (IG), Gain Ratio (GR), Symmetrical Uncertainty (SU), RelieF (RF) and RF-W. Two high ranked features from these selected features are extracted by the Modified Social Ski-Driver optimization (MSSO) algorithm. With that high ranked features, the liver cancer tissues are accurately detected by Sunflower Optimization-based deep neural network (DSFNN) approach. The analysis part concludes that a miRNA biomarker having a higher rank provide better cancer detection results than other low-ranked biomarkers. In this work, 10 different, clinically verified miRNA biomarkers are selected for this detection process. The data required for liver cancer detection is selected from NCBI-GEO database. The performance of this entire cancer detection process is evaluated by accuracy, sensitivity, precision, specificity, and Area under curve (AUC) metrics. Furthermore, we also determined that the usage of 10, 5, and 3 clinically verified miRNAs provide better cancer detection results than other miRNAs. Among all clinically verified miRNAs, the selected three biomarkers (hsa-mir-10b, hsa-let-7c, hsa-mir- 145) has attained higher recognition result. The performance result attained by the proposed DSFNN is compared with five different algorithms for both training and validation datasets.

3

Deep learning application on object tracking

84%

Taglout R. , Saoud B.

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tom R. 99, nr 9

145--149

EN

The challenge of correctly identifying the target in the first frame of continuous sequences and tracking it in succeeding frames is frequently solved by visual tracking. The development of deep neural networks has aided in significant advancement over the past few decades. However, they are still considerable challenges in developing reliable trackers in challenging situations, essentially due to complicated backgrounds, partial or complete occlusion, illumination change, blur and similar objects. In this paper, we study correlation filter and deep learning-based approaches. We have compared the following trackers ECO, SaimRPN, ATOM, DiMP, TRASFUST and TREG. These trackers have been developed based on deep neural networks and are very recent. Performances of trackers have been evaluated on OTB-100, UAV123, VOT 2019, GOT-10k and LaSOT dataset. Results prove the effectiveness of deep neural networks to cope up with object tracking in videos.

PL

Wyzwanie polegające na prawidłowej identyfikacji celu w pierwszej klatce ciągłych sekwencji i śledzeniu go w kolejnych klatkach jest często rozwiązywane przez śledzenie wizualne. Rozwój głębokich sieci neuronowych przyczynił się do znacznego postępu w ciągu ostatnich kilku dekad. Jednak nadal stanowią one poważne wyzwanie w opracowywaniu niezawodnych trackerów w trudnych sytuacjach, głównie ze względu na skomplikowane tła, częściowe lub całkowite przesłonięcie, zmiany oświetlenia, rozmycie i podobne obiekty. W tym artykule badamy filtr korelacji i podejście oparte na głębokim uczeniu się. Porównaliśmy następujące trackery ECO, SaimRPN, ATOM, DiMP, TRASFUST i TREG. Te trackery zostały opracowane w oparciu o głębokie sieci neuronowe i są bardzo nowe. Wydajność trackerów została oceniona na zestawie danych OTB-100, UAV123, VOT 2019, GOT-10k i LaSOT. Wyniki dowodzą skuteczności głębokich sieci neuronowych w radzeniu sobie ze śledzeniem obiektów w filmach.

4

Deep Neural Networks for Curbing Climate Change-Induced Farmers-Herdsmen Clashes in a Sustainable Social Inclusion Initiative

84%

Okewu E. , Misra S. , Fernandez S. L. , Ayeni F. , Mbarika V. , Damaševičius R.

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tom Vol. 14, nr 2

143--155

EN

Peaceful coexistence of farmers and pastoralists is becoming increasingly elusive and has adverse impact on agricultural revolution and global food security. The targets of Sustainable Development Goal 16 (SDG 16) include promoting peaceful and inclusive societies for sustainable development, providing access to justice for all and building effective, accountable and inclusive institutions at all levels. As a soft approach and long term solution to the perennial farmers-herdsmen clashes with attendant humanitarian crisis, this study proposes a social inclusion architecture using deep neural network (DNN). This is against the backdrop that formulating policies and implementing programmes based on unbiased information obtained from historical agricultural data using intelligent technology like deep neural network (DNN) can be handy in managing emotions. In this vision paper, a DNN-based Farmers-Herdsmen Expert System (FHES) is proposed based on data obtained from the Nigerian National Bureau of Statistics for tackling the incessant climate change-induced farmers-herdsmen clashes, with particular reference to Nigeria. So far, many lives have been lost. FHES is modelled as a deep neural network and trained using farmers-herdsmen historical data. Input variables used include land, water, vegetation, and implements while the output is farmers/herders disposition to peace. Regression analysis and pattern recognition performed by the DNN on the farmers-herdsmen data will enrich the inference engine of FHES with extracted rules (knowledge base). This knowledge base is then relied upon to classify future behaviours of herdsmen/farmers as well as predict their dispositions to violence. Critical stakeholders like governments, service providers and researchers can leverage on such advisory to initiate proactive and socially inclusive conflict prevention measures such as people-friendly policies, programmes and legislations. This way, conflicts can be averted, national security challenges tackled, and peaceful atmosphere guaranteed for sustainable development.

PL

Pokojowe współistnienie rolników i pasterzy staje się coraz mnie realne, co ma negatywny wpływ na rewolucję rolniczą i globalne bezpieczeństwo żywnościowe. Cele zrównoważonego rozwoju (SDG 16) obejmują promowanie tworzenia pokojowych i zintegrowanych społeczeństw na rzecz zrównoważonego rozwoju, zapewnienie wszystkim dostępu do uczciwego wymiaru sprawiedliwości i tworzenie skutecznych, odpowiedzialnych i integrujących instytucji na wszystkich poziomach. W ramach łagodnego podejścia i długofalowego podejścia do problemu konfliktów rolników-pasterzy w kontekście kryzysu humanitarnego, w niniejszym artykule zaproponowano architekturę integracji społecznej wykorzystującą głęboką sieć neuronową (DNN). Formułowanie polityki i wdrażanie programów w oparciu o obiektywne informacje uzyskane z historycznych danych przy użyciu inteligentnej technologii, takiej jak głęboka sieć neuronowa (DNN), może być przydatne w zarządzaniu emocjami. W niniejszym artykule zaproponowano oparty na danych uzyskanych od Nigeryjskiego Narodowego Urzędu Statystycznego system ekspercki rolników-pasterzy (FHES) oparty na DNN w celu przeciwdziałaniu nieustannym starciom rolników-pasterzy wywołanych zmianami klimatu, ze szczególnym uwzględnieniem Nigerii. Do tej pory wiele było ofiar. System FHES jest modelowany jako głęboka sieć neuronowa, przy użyciu danych historycznych hodowców-pasterzy. Zastosowane zmienne wejściowe obejmują ziemię, wodę, roślinność i narzędzia, podczas gdy zmienne wyjściowe to rolnicy-pasterze skłonni do pokoju. Analiza regresji i rozpoznawanie wzorców przeprowadzone przez DNN na danych rolników-pasterzy wzbogaci mechanizm wnioskowania systemu FHES o wyodrębnione reguły (baza wiedzy). Podstawą tej wiedzy jest klasyfikacja przyszłych zachowań pasterzy/rolników, a także przewidywanie ich skłonności do przemocy. Krytyczni interesariusze, tacy jak rządy, dostawcy usług i naukowcy, mogą wykorzystać takie doradztwo do zainicjowania proaktywnych i społecznie włączających środków zapobiegania konfliktom, takich jak przyjazne dla ludzi polityki, programy i prawodawstwo. W ten sposób można uniknąć konfliktów, stawić czoła wyzwaniom bezpieczeństwa narodowego i zagwarantować pokojową atmosferę dla zrównoważonego rozwoju.

5

Breast cancer detection from histopathology images using modified residual neural networks

67%

Gupta V. , Vasudev M. , Doegar A. , Sambyal N.

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2021

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tom Vol. 41, no. 4

1272--1287

EN

Breast cancer is one of the major causes of death among women worldwide. Efficient diagnosis of breast cancer in the early phases can reduce the associated morbidity and mortality and can provide a higher probability of full recovery. Computer-aided detection systems use computer technologies to detect abnormalities in clinical images which can assist medical professionals in a faster and more accurate diagnosis. In this paper, we propose a modified residual neural network-based method for breast cancer detection using histopathology images. The proposed approach provides good performance over varying magnification factors of 40X, 100X, 200X and 400X. The network obtains an average classification accuracy of 99.75%, precision of 99.18% and recall of 99.37% on BreakHis dataset with 40X magnification factor. The proposed work outperforms the existing methods and delivers state-of-the-art results on the benchmark breast cancer dataset.

6

Deep-neural-networks-based approaches for Biot-squirt model in rock physics

67%

Xiong F. , Liu J. , Guo Z. , Liu J.

Acta Geophysica

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2022

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tom Vol. 70, no 2

593--607

EN

A new cost-effective surrogate model using deep neural network (DNN) for seismic wave propagation in rocks saturated with fluid is presented. In this field, the dispersion/attenuation analysis and wave-field simulation are two key measurements which can be carried out by solving wave equations. The Biot–squirt (BISQ) equation is a classical wave propagation model in geophysical forward modeling and has been widely used. The solution of such equation, especially by numerical method, is often complex and time-consuming. In this work, a DNN model is trained with the dataset of velocity and inverse quality factor generated from BISQ model. The results show that the relative mean square error between the predictions of DNN model and that of BISQ model on the test sets are all less than 3%. It indicates that the DNN model has learned the high-dimensional space well and then can realize the dispersion/attenuation analysis for any given rock physical parameters. Besides, the other well-trained DNN model is used to obtain the simulation results with second-order accuracy according to results by finite difference scheme with first-order accuracy. It reveals that the fast wave-field simulation can be implemented once the results with lower accuracy are obtained.

7

Deep neural network and ANN ensemble for slope stability prediction

59%

Gupta A. , Aggarwal Y. , Aggarwal P.

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tom Vol. 116, nr 1

14--27

EN

Purpose: Application of deep neural networks (DNN) and ensemble of ANN with bagging for estimating of factor of safety (FOS) of soil stability with a comparative performance analysis done for all techniques. Design/methodology/approach: 1000 cases with different geotechnical and similar Geometrical properties were collected and analysed using the Limit Equilibrium based Morgenstern-Price Method with input variables as the strength parameters of the soil layers, i.e., Su (Upper Clay), Su (Lower Clay), Su (Peat), angle of internal friction (φ), Su (Embankment) with the factor of safety (FOS) as output. The evaluation and comparison of the performance of predicted models with cross-validation having ten folds were made based on correlation-coefficient (CC), Nash-Sutcliffe-model efficiency-coefficient (NSE), root-mean-square-error (RMSE), mean-absolute-error (MAE) and scattering-index (S.I.). Sensitivity analysis was conducted for the effects of input variables on FOS of soil stability based on their importance. Findings: The results showed that these techniques have great capability and reflect that the proposed model by DNN can enhance performance of the model, surpassing ensemble in prediction. The Sensitivity analysis outcome demonstrated that Su (Lower Clay) significantly affected the factor of safety (FOS), trailed by Su (Peat). Research limitations/implications: This paper sets sight on use of deep neural network (DNN) and ensemble of ANN with bagging for estimating of factor of safety (FOS) of soil stability. The current approach helps to understand the tangled relationship of various inputs to estimate the factor of safety of soil stability using DNN and ensemble of ANN with bagging. Practical implications: A dependable prediction tool is provided, which suggests that model can help scientists and engineers optimise FOS of soil stability. Originality/value: Recently, DNN and ensemble of ANN with bagging have been used in various civil engineering problems as reported by several studies and has also been observed to be outperforming the current prevalent modelling techniques. DNN can signify extremely changing and intricate high-dimensional functions in correlation to conventional neural networks. But on a detailed literature review, the application of these techniques to estimate factor of safety of soil stability has not been observed.