A Novel Image Classification Method with CNN-XGBoost Model

Ren, Xuhua; Guo, Haonan; Li, Shenghong; Wang, Shilin; Li, Jianhua

doi:10.1007/978-3-319-64185-0_28

Cited by 102 publications

(53 citation statements)

References 14 publications

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“…Since then XGBoost has been successfully used for different classification problems. Its superiority for LC and LU classification from very high resolution images was also shown in recent studies [58,62].…”

Section: Rs Data Analsyissupporting

confidence: 55%

“…The gradient boosting method (GBM) is a supervised classification technique and belongs to regression and classification trees models [56,57]. Tree boosting is an ensemble learning algorithm that is very effective in the classification of even weak trees [58], as has been shown in scene classification [59,60]. However, traditional GBMs require the tuning of a number of parameters and are thus more susceptible to overfitting than other ML algorithms, such as SVM.…”

Section: Rs Data Analsyismentioning

confidence: 99%

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Evaluating Resilience-Centered Development Interventions with Remote Sensing

Kerle

Ghaffarian

Nawrotzki³

et al. 2019

Remote Sensing

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Natural disasters are projected to increase in number and severity, in part due to climate change. At the same time a growing number of disaster risk reduction (DRR) and climate change adaptation measures are being implemented by governmental and non-governmental organizations, and substantial post-disaster donations are frequently pledged. At the same time there has been increasing demand for transparency and accountability, and thus evidence of those measures having a positive effect. We hypothesized that resilience-enhancing interventions should result in less damage during a hazard event, or at least quicker recovery. In this study we assessed recovery over a 3 year period of seven municipalities in the central Philippines devastated by Typhoon Haiyan in 2013. We used very high resolution optical images (<1 m), and created detailed land cover and land use maps for four epochs before and after the event, using a machine learning approach with extreme gradient boosting. The spatially and temporally highly variable recovery maps were then statistically related to detailed questionnaire data acquired by DEval in 2012 and 2016, whose principal aim was to assess the impact of a 10 year land-planning intervention program by the German agency for technical cooperation (GIZ). The survey data allowed very detailed insights into DRR-related perspectives, motivations and drivers of the affected population. To some extent they also helped to overcome the principal limitation of remote sensing, which can effectively describe but not explain the reasons for differential recovery. However, while a number of causal links between intervention parameters and reconstruction was found, the common notion that a resilient community should recover better and more quickly could not be confirmed. The study also revealed a number of methodological limitations, such as the high cost for commercial image data not matching the spatially extensive but also detailed scale of field evaluations, the remote sensing analysis likely overestimating damage and thus providing incorrect recovery metrics, and image data catalogues especially for more remote communities often being incomplete. Nevertheless, the study provides a valuable proof of concept for the synergies resulting from an integration of socio-economic survey data and remote sensing imagery for recovery assessment.

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Section: Rs Data Analsyissupporting

confidence: 55%

Section: Rs Data Analsyismentioning

confidence: 99%

Evaluating Resilience-Centered Development Interventions with Remote Sensing

Kerle

Ghaffarian

Nawrotzki³

et al. 2019

Remote Sensing

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show abstract

“…As suggested in previous studies, RF is a powerful classifier for classifying gene expression data ( Wu et al, 2003 ; Lee et al, 2005 ; Ishwaran et al, 2010 ). And XGBoost keeps winning in “every” Kaggle competition and has become a really popular tool among data scientists ( Ren et al, 2017 ; Torlay et al, 2017 ; Zhang and Zhan, 2017 ). Recently, XGBoost has been successfully applied to many classification problems, such as pan-cancer classification ( Li et al, 2017 ) and prediction of RNA-protein interactions ( Jain et al, 2018 ).…”

Section: Discussionmentioning

confidence: 99%

Identification of miR-200c and miR141-Mediated lncRNA-mRNA Crosstalks in Muscle-Invasive Bladder Cancer Subtypes

et al. 2018

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Basal and luminal subtypes of muscle-invasive bladder cancer (MIBC) have distinct molecular profiles and heterogeneous clinical behaviors. The interactions between mRNAs and lncRNAs, which might be regulated by miRNAs, have crucial roles in many cancers. However, the miRNA-dependent crosstalk between lncRNA and mRNA in specific MIBC subtypes still remains unclear. In this study, we first classified MIBC into two conservative subtypes using miRNA, mRNA and lncRNA expression data derived from The Cancer Genome Atlas. Then we investigated subtype-related biological pathways and evaluated the subtype classification performance using Decision Trees, Random Forest and eXtreme Gradient Boosting (XGBoost). At last, we explored potential miRNA-mediated lncRNA-mRNA crosstalks based on co-expression analysis. Our results show that: (1) the luminal subtype is primarily characterized by upregulation of metabolism-related pathways while the basal subtype is predominantly characterized by upregulation of epithelial-mesenchymal transition, metastasis, and immune system process-related pathways; (2) the XGBoost prediction model is consistently robust for classification of the molecular subtypes of MIBC across four datasets (The area under the ROC curve > 0.9); (3) the expression levels of the molecules in the miR-200c and miR141-mediated lncRNA-mRNA crosstalks differ considerably between the two subtypes and have close relationships with the prognosis of MIBC. The miR-200c and miR-141-dependent mRNA-lncRNA crosstalks might be of great significance in tumorigenesis and tumor progression and may serve as the novel prognostic predictors and classification markers of MIBC subtypes.

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“…A extração de característicasé o processo mais importante no desenvolvimento de um sistema automático para classificação de imagens [Ren et al 2017]. Nosúltimos anos, as CNNs, que são modelos de Deep Learning foram propostos para etapa de extração de recursos em imagens.…”

Section: Extração De Característicasunclassified

Classification of CT images in COVID-19 and Non-COVID-19 using CNN to extract features and multiple classifiers

Carvalho¹,

Carvalho²

2020

Anais Da Escola Regional De Computação Ceará, Maranhão, Piauí (ERCEMAPI 2020)

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O COVID-19 é uma doença respiratória que já infectou mais de 12.3 milhões de pessoas em todo o mundo e é responsável por mais de 556.300 mortes. O diagnóstico precoce do COVID-19 é essencial para a cura e controle da doença. A tomografia computadorizada (TC) apresentou resultados eficientes na avaliação de pacientes com suspeita de infecção por COVID-19. A análise da TC requer o esforço de um especialista, o que pode levar a erros de diagnóstico. O uso de sistemas de diagnóstico auxiliado por computador pode minimizar os problemas gerados pela análise de TCs por especialistas. Este artigo apresenta uma metodologia para diagnosticar a COVID-19 usando CNN para extração de características e múltiplos classificadores em imagens de TC. A metodologia apresentou uma acurácia de 99,79%, recall de 99,79%, precisão de 99,80%, F-score de 0,997, AUC de 0,997 e índice kappa de 0,995. Os resultados obtidos mostram que a metodologia proposta pode ser utilizada como um sistema de auxílio ao diagnóstico.

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A Novel Image Classification Method with CNN-XGBoost Model

Cited by 102 publications

References 14 publications

Evaluating Resilience-Centered Development Interventions with Remote Sensing

Evaluating Resilience-Centered Development Interventions with Remote Sensing

Identification of miR-200c and miR141-Mediated lncRNA-mRNA Crosstalks in Muscle-Invasive Bladder Cancer Subtypes

Classification of CT images in COVID-19 and Non-COVID-19 using CNN to extract features and multiple classifiers

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