Interpretability of a Deep Learning Model for Rodents Brain Semantic Segmentation

2019 ◽  
pp. 307-318
Author(s):  
Leonardo Nogueira Matos ◽  
Mariana Fontainhas Rodrigues ◽  
Ricardo Magalhães ◽  
Victor Alves ◽  
Paulo Novais
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
Learning Model ◽  
Deep Learning Model

scholarly journals Semantic segmentation of PolSAR image data using advanced deep learning model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rajat Garg ◽  
Anil Kumar ◽  
Nikunj Bansal ◽  
Manish Prateek ◽  
Shashi Kumar
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Urban Area ◽  
Urban Areas ◽  
Learning Algorithms ◽  
Semantic Segmentation ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Deep Learning Model

AbstractUrban area mapping is an important application of remote sensing which aims at both estimation and change in land cover under the urban area. A major challenge being faced while analyzing Synthetic Aperture Radar (SAR) based remote sensing data is that there is a lot of similarity between highly vegetated urban areas and oriented urban targets with that of actual vegetation. This similarity between some urban areas and vegetation leads to misclassification of the urban area into forest cover. The present work is a precursor study for the dual-frequency L and S-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission and aims at minimizing the misclassification of such highly vegetated and oriented urban targets into vegetation class with the help of deep learning. In this study, three machine learning algorithms Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) have been implemented along with a deep learning model DeepLabv3+ for semantic segmentation of Polarimetric SAR (PolSAR) data. It is a general perception that a large dataset is required for the successful implementation of any deep learning model but in the field of SAR based remote sensing, a major issue is the unavailability of a large benchmark labeled dataset for the implementation of deep learning algorithms from scratch. In current work, it has been shown that a pre-trained deep learning model DeepLabv3+ outperforms the machine learning algorithms for land use and land cover (LULC) classification task even with a small dataset using transfer learning. The highest pixel accuracy of 87.78% and overall pixel accuracy of 85.65% have been achieved with DeepLabv3+ and Random Forest performs best among the machine learning algorithms with overall pixel accuracy of 77.91% while SVM and KNN trail with an overall accuracy of 77.01% and 76.47% respectively. The highest precision of 0.9228 is recorded for the urban class for semantic segmentation task with DeepLabv3+ while machine learning algorithms SVM and RF gave comparable results with a precision of 0.8977 and 0.8958 respectively.

scholarly journals Efficient 3D Deep Learning Model for Medical Image Semantic Segmentation

2021 ◽  
Vol 60 (1) ◽  
pp. 1231-1239
Author(s):  
Nasser Alalwan ◽  
Amr Abozeid ◽  
AbdAllah A. ElHabshy ◽  
Ahmed Alzahrani
Keyword(s):  
Deep Learning ◽  
Medical Image ◽  
Semantic Segmentation ◽  
Learning Model ◽  
Deep Learning Model

scholarly journals TOWARDS MESH-BASED DEEP LEARNING FOR SEMANTIC SEGMENTATION IN PHOTOGRAMMETRY

2021 ◽  
Vol V-2-2021 ◽  
pp. 59-66
Author(s):  
M. Knott ◽  
R. Groenendijk
Keyword(s):  
Deep Learning ◽  
Search Algorithm ◽  
Predictive Performance ◽  
Semantic Segmentation ◽  
Learning Task ◽  
Learning Model ◽  
Breadth First Search ◽  
Data Set ◽  
Processing Step ◽  
Deep Learning Model

Abstract. This research is the first to apply MeshCNN – a deep learning model that is specifically designed for 3D triangular meshes – in the photogrammetry domain. We highlight the challenges that arise when applying a mesh-based deep learning model to a photogrammetric mesh, especially w.r.t. data set properties. We provide solutions on how to prepare a remotely sensed mesh for a machine learning task. The most notable pre-processing step proposed is a novel application of the Breadth-First Search algorithm for chunking a large mesh into computable pieces. Furthermore, this work extends MeshCNN such that photometric features based on the mesh texture are considered in addition to the geometric information. Experiments show that including color information improves the predictive performance of the model by a large margin. Besides, experimental results indicate that segmentation performance could be advanced substantially with the introduction of a high-quality benchmark for semantic segmentation on meshes.

scholarly journals A Plug-and-play Attention Module for CT-Based COVID-19 Segmentation

2021 ◽  
Vol 2078 (1) ◽  
pp. 012041
Author(s):  
Zhengqian Zhang ◽  
Haoqian Xue ◽  
Guanglu Zhou
Keyword(s):  
Deep Learning ◽  
Spatial Attention ◽  
Semantic Segmentation ◽  
Learning Model ◽  
Context Information ◽  
Disease Area ◽  
Plug And Play ◽  
New Type ◽  
The Relationship ◽  
Deep Learning Model

Abstract At the end of 2019, a new type of coronavirus (COVID-19) rapidly spread globally, even if the penetration of vaccination is getting higher and higher, the emergence of viral variants has increased the number of new coronal pneumonia infections. The deep learning model can help doctors quickly and accurately divide the lesion zone. However, there are many problems in the segmentation of the slice from the CT slice, including the problem of uncertainty of the disease area, low accuracy. At the same time, the semantic segmentation model of the traditional CNN architecture has natural defects, and the sensing field restrictions result in constructing the relationship between pixels and pixels, and the context information is insufficient. In order to solve the above problems, we introduced a Transformer module. Visual Transformer has been proved to effectively improve the accuracy of the model. We have designed a plug-and-play spatial attention module, on the basis of attention, increased positional offset, effective aggregate advanced features, and improve the accuracy of existing models.

scholarly journals Estimation of Rice Lodging Using Semantic Segmentation Based on Deep Learning Model

2021 ◽  
Vol 22 (9) ◽  
pp. 28-34
Author(s):  
Byoungjun Kim ◽  
Keunho Park ◽  
Hyung-geun Ahn ◽  
Kee-yeun Kim ◽  
Sunghwan Jeong
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
Learning Model ◽  
Deep Learning Model

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

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