Convolutional Neural Networks for the Classification of the Microstructure of Tight Sandstone

2021 ◽  
Author(s):  
Ana Gabriela Reyna Flores ◽  
Quentin Fisher ◽  
Piroska Lorinczi
Keyword(s):  
Electron Microscopy ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Model Performance ◽  
Area Under The Curve ◽  
Tight Gas ◽  
Classification Problems ◽  
Tight Sandstone ◽  
Permeability Prediction ◽  
Rock Microstructure

Abstract Tight gas sandstone reservoirs vary widely in terms of rock type, depositional environment, mineralogy and petrophysical properties. For this reason, estimating their permeability is a challenge when core is not available because it is a property that cannot be measured directly from wire-line logs. The aim of this work is to create an automatic tool for rock microstructure classification as a first step for future permeability prediction. Permeability can be estimated from porosity measured using wire-line data such as derived from density-neutron tools. However, without additional information this is highly inaccurate because porosity-permeability relationships are controlled by the microstructure of samples and permeability can vary by over five orders of magnitude. Experts can broadly estimate porosity-permeability relationships by analysing the microstructure of rocks using Scanning Electron Microscopy (SEM) or optical microscopy. Such estimates are, however, subjective and require many years of experience. A Machine Learning model for the automation of rock microstructure determination on tight gas sandstones has been built using Convolutional Neural Networks (CNNs) and trained on backscattered images from cuttings. Current results were obtained by training the model on around 24,000 Back Scattering Electron Microscopy (BSEM) images from 25 different rock samples. The obtained model performance for the current dataset are 97% of average of both validation and test categorical accuracy. Also, loss of 0.09 and 0.089 were obtained for validation and test correspondingly. Such high accuracy and low loss indicate an overall great model performance. Other metrics and debugging techniques such Gradient-weighted Class Activation Mapping (Grad-CAM), Receiver Operator Characteristics (ROC) and Area Under the Curve (AUC) were considered for the model performance evaluation obtaining positive results. Nevertheless, this can be improved by obtaining images from new already available samples and make the model generalizes better. Current results indicate that CNNs are a powerful tool and their application over thin section images is an answer for image analysis and classification problems. The use of this classifier removes the subjectivity of estimating porosity-permeability relationships from microstructure and can be used by non-experts. The current results also open the possibility of a data driven permeability prediction based on rock microstructure and porosity from well logs.

Methodology for Classifying Diseases in Plants Using Convolutional Neural Networks

2021 ◽  
Vol 18 (2) ◽  
pp. 27-39
Author(s):  
Michel Costa ◽  
◽  
Vanessa Rezende ◽  
Cledisson Martins ◽  
Adam Santos ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Area Under The Curve ◽  
Test Accuracy ◽  
Classification Problems ◽  
Learning Techniques ◽  
Nonparametric Statistical Test ◽  
Post Hoc ◽  
Nonparametric Statistical

Convolutional neural networks (CNNs) are one of the deep learning techniques that, due to the computational advance of the last few years, have leveraged the area of computer vision, allowing substantial gains in the most varied classification problems, especially those involving digital images. In this context, this paper aims to propose a methodology for the classification of multiple pathologies related to different plant species. Initially, this methodology involved the image processing and the generation of ten new databases, varying between 50 and 66 classes with greater representation. After training the models (VGG16, RestNet101v1, ResNet101v2, ResNetXt50, and DenseNet169), a comparative study was conducted based on widely used classification metrics, such as test accuracy, f1-score, and area under the curve. To attest the significance of the results, Friedman’s nonparametric statistical test and two post-hoc procedures were performed, which demonstrated that ResNetXt50 and DenseNet169 obtained superior performances when compared with VGG16 and ResNets.

Automatic Evaluation of Histological Prognostic Factors Using Two Consecutive Convolutional Neural Networks on Kidney Samples

2021 ◽  
pp. CJN.07830621
Author(s):  
Elise Marechal ◽  
Adrien Jaugey ◽  
Georges Tarris ◽  
Michel Paindavoine ◽  
Jean Seibel ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Interstitial Fibrosis ◽  
Kidney Tissue ◽  
Area Under The Curve ◽  
Tubular Atrophy ◽  
Prognostic Features ◽  
Glomerular Volume ◽  
Luminal Stenosis ◽  
Histological Data

Background and Objectives: The prognosis of patients undergoing kidney tumor resection or kidney donation is linked to many histological criteria. These criteria notably include glomerular density, glomerular volume, vascular luminal stenosis, and severity of interstitial fibrosis/tubular atrophy. Automated measurements through a Deep Learning approach could save time and provide more precise data. This work aimed to develop a free tool to automatically obtain kidney histological prognostic features. Design, setting, participants, and measurements: Two hundred and forty one samples of healthy kidney tissue were split into 3 independent cohorts. The "Training" cohort (n=65) was used to train two Convolutional Neural Networks: one to detect the cortex and a second one to segment the kidney structures. The "Test" cohort (n=50) assessed their performances by comparing manually outlined regions of interest to predicted ones. The "Application" cohort (n=126) compared prognostic histological data obtained manually or through the algorithm based on the combination of the two Convolutional Neural Networks. Results: In the Test cohort, the networks isolated the cortex and segmented the elements of interest with good performances (more than 90% of the cortex, healthy tubules, glomeruli, and even globally sclerotic glomeruli were detected). In the Application cohort, the expected and predicted prognostic data were significantly correlated. The correlation coefficients r were respectively 0.85 for glomerular volume, 0.51 for glomerular density, 0.75 for interstitial fibrosis, 0.71 for tubular atrophy, and 0.73 for vascular intimal thickness. The algorithm had a good ability to predict significant (> 25%) tubular atrophy and interstitial fibrosis level (ROC curve with an area under the curve 0.92 and 0.91, respectively) or a significant vascular luminal stenosis (> 50%) (area under the curve 0.85). Conclusion: This freely available tool enables the automated segmentation of kidney tissue to obtain prognostic histological data in a fast, objective, reliable and reproducible way.

scholarly journals Incremental Dilations Using CNN for Brain Tumor Classification

2020 ◽  
Vol 10 (14) ◽  
pp. 4915 ◽  
Author(s):  
Sanjiban Sekhar Roy ◽  
Nishant Rodrigues ◽  
Y-h. Taguchi
Keyword(s):  
Neural Networks ◽  
Brain Tumor ◽  
Convolutional Neural Networks ◽  
Model Performance ◽  
Classification Performance ◽  
Tumor Classification ◽  
Computational Overhead ◽  
Performance Accuracy ◽  
Artificial Brain ◽  
Dilation Rate

Brain tumor classification is a challenging task in the field of medical image processing. Technology has now enabled medical doctors to have additional aid for diagnosis. We aim to classify brain tumors using MRI images, which were collected from anonymous patients and artificial brain simulators. In this article, we carry out a comparative study between Simple Artificial Neural Networks with dropout, Basic Convolutional Neural Networks (CNN), and Dilated Convolutional Neural Networks. The experimental results shed light on the high classification performance (accuracy 97%) of Dilated CNN. On the other hand, Dilated CNN suffers from the gridding phenomenon. An incremental, even number dilation rate takes advantage of the reduced computational overhead and also overcomes the adverse effects of gridding. Comparative analysis between different combinations of dilation rates for the different convolution layers, help validate the results. The computational overhead in terms of efficiency for training the model to reach an acceptable threshold accuracy of 90% is another parameter to compare the model performance.

scholarly journals Detection of COVID-19 from Chest X-Ray Images Using Convolutional Neural Networks

2020 ◽  
Vol 25 (6) ◽  
pp. 553-565 ◽  
Author(s):  
Boran Sekeroglu ◽  
Ilker Ozsahin
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Statistical Data ◽  
Area Under The Curve ◽  
Operating Characteristics ◽  
X Ray ◽  
Life Saving ◽  
Chest X Ray

The detection of severe acute respiratory syndrome coronavirus 2 (SARS CoV-2), which is responsible for coronavirus disease 2019 (COVID-19), using chest X-ray images has life-saving importance for both patients and doctors. In addition, in countries that are unable to purchase laboratory kits for testing, this becomes even more vital. In this study, we aimed to present the use of deep learning for the high-accuracy detection of COVID-19 using chest X-ray images. Publicly available X-ray images (1583 healthy, 4292 pneumonia, and 225 confirmed COVID-19) were used in the experiments, which involved the training of deep learning and machine learning classifiers. Thirty-eight experiments were performed using convolutional neural networks, 10 experiments were performed using five machine learning models, and 14 experiments were performed using the state-of-the-art pre-trained networks for transfer learning. Images and statistical data were considered separately in the experiments to evaluate the performances of models, and eightfold cross-validation was used. A mean sensitivity of 93.84%, mean specificity of 99.18%, mean accuracy of 98.50%, and mean receiver operating characteristics–area under the curve scores of 96.51% are achieved. A convolutional neural network without pre-processing and with minimized layers is capable of detecting COVID-19 in a limited number of, and in imbalanced, chest X-ray images.

scholarly journals Fusion High-Resolution Network for Diagnosing ChestX-ray Images

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 190 ◽  
Author(s):  
Zhiwei Huang ◽  
Jinzhao Lin ◽  
Liming Xu ◽  
Huiqian Wang ◽  
Tong Bai ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
High Resolution ◽  
Convolutional Neural Networks ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Disease Classification ◽  
Deep Convolutional Neural Networks ◽  
Learning Methods ◽  
Global And Local

The application of deep convolutional neural networks (CNN) in the field of medical image processing has attracted extensive attention and demonstrated remarkable progress. An increasing number of deep learning methods have been devoted to classifying ChestX-ray (CXR) images, and most of the existing deep learning methods are based on classic pretrained models, trained by global ChestX-ray images. In this paper, we are interested in diagnosing ChestX-ray images using our proposed Fusion High-Resolution Network (FHRNet). The FHRNet concatenates the global average pooling layers of the global and local feature extractors—it consists of three branch convolutional neural networks and is fine-tuned for thorax disease classification. Compared with the results of other available methods, our experimental results showed that the proposed model yields a better disease classification performance for the ChestX-ray 14 dataset, according to the receiver operating characteristic curve and area-under-the-curve score. An ablation study further confirmed the effectiveness of the global and local branch networks in improving the classification accuracy of thorax diseases.

scholarly journals Evolving Deep Convolutional Neural Networks by Variable-Length Particle Swarm Optimization for Image Classification

2021 ◽  
Author(s):  
Bin Wang ◽  
Y Sun ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Neural Networks ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Particle Swarm ◽  
The State ◽  
Variable Length ◽  
Classification Error ◽  
Classification Problems ◽  
Deep Convolutional Neural Networks ◽  
State Of Art

© 2018 IEEE. Convolutional neural networks (CNNs) are one of the most effective deep learning methods to solve image classification problems, but the best architecture of a CNN to solve a specific problem can be extremely complicated and hard to design. This paper focuses on utilising Particle Swarm Optimisation (PSO) to automatically search for the optimal architecture of CNNs without any manual work involved. In order to achieve the goal, three improvements are made based on traditional PSO. First, a novel encoding strategy inspired by computer networks which empowers particle vectors to easily encode CNN layers is proposed; Second, in order to allow the proposed method to learn variable-length CNN architectures, a Disabled layer is designed to hide some dimensions of the particle vector to achieve variable-length particles; Third, since the learning process on large data is slow, partial datasets are randomly picked for the evaluation to dramatically speed it up. The proposed algorithm is examined and compared with 12 existing algorithms including the state-of-art methods on three widely used image classification benchmark datasets. The experimental results show that the proposed algorithm is a strong competitor to the state-of-art algorithms in terms of classification error. This is the first work using PSO for automatically evolving the architectures of CNNs. © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

scholarly journals Efficient Weights Quantization of Convolutional Neural Networks Using Kernel Density Estimation based Non-uniform Quantizer

2019 ◽  
Vol 9 (12) ◽  
pp. 2559 ◽  
Author(s):  
Sanghyun Seo ◽  
Juntae Kim
Keyword(s):  
Neural Networks ◽  
Density Estimation ◽  
Convolutional Neural Networks ◽  
Kernel Density Estimation ◽  
High Performance ◽  
Model Performance ◽  
Kernel Density ◽  
Memory Space ◽  
Computational Costs ◽  
Uniform Quantization

Convolutional neural networks (CNN) have achieved excellent results in the field of image recognition that classifies objects in images. A typical CNN consists of a deep architecture that uses a large number of weights and layers to achieve high performance. CNN requires relatively large memory space and computational costs, which not only increase the time to train the model but also limit the real-time application of the trained model. For this reason, various neural network compression methodologies have been studied to efficiently use CNN in small embedded hardware such as mobile and edge devices. In this paper, we propose a kernel density estimation based non-uniform quantization methodology that can perform compression efficiently. The proposed method performs efficient weights quantization using a significantly smaller number of sampled weights than the number of original weights. Four-bit quantization experiments on the classification of the ImageNet dataset with various CNN architectures show that the proposed methodology can perform weights quantization efficiently in terms of computational costs without significant reduction in model performance.

scholarly journals Urinary Stone Detection on CT Images Using Deep Convolutional Neural Networks: Evaluation of Model Performance and Generalization

2019 ◽  
Vol 1 (4) ◽  
pp. e180066 ◽  
Author(s):  
Anushri Parakh ◽  
Hyunkwang Lee ◽  
Jeong Hyun Lee ◽  
Brian H. Eisner ◽  
Dushyant V. Sahani ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Model Performance ◽  
Urinary Stone ◽  
Ct Images ◽  
Deep Convolutional Neural Networks ◽  
Stone Detection

scholarly journals Text Classification Based on Convolutional Neural Networks and Word Embedding for Low-Resource Languages: Tigrinya

Information ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 52
Author(s):  
Awet Fesseha ◽  
Shengwu Xiong ◽  
Eshete Derb Emiru ◽  
Moussa Diallo ◽  
Abdelghani Dahou
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Text Classification ◽  
Word Embedding ◽  
Northern Ethiopia ◽  
Classification Problems ◽  
Semitic Languages ◽  
Low Resource ◽  
Free Data ◽  
Class Labels

This article studies convolutional neural networks for Tigrinya (also referred to as Tigrigna), which is a family of Semitic languages spoken in Eritrea and northern Ethiopia. Tigrinya is a “low-resource” language and is notable in terms of the absence of comprehensive and free data. Furthermore, it is characterized as one of the most semantically and syntactically complex languages in the world, similar to other Semitic languages. To the best of our knowledge, no previous research has been conducted on the state-of-the-art embedding technique that is shown here. We investigate which word representation methods perform better in terms of learning for single-label text classification problems, which are common when dealing with morphologically rich and complex languages. Manually annotated datasets are used here, where one contains 30,000 Tigrinya news texts from various sources with six categories of “sport”, “agriculture”, “politics”, “religion”, “education”, and “health” and one unannotated corpus that contains more than six million words. In this paper, we explore pretrained word embedding architectures using various convolutional neural networks (CNNs) to predict class labels. We construct a CNN with a continuous bag-of-words (CBOW) method, a CNN with a skip-gram method, and CNNs with and without word2vec and FastText to evaluate Tigrinya news articles. We also compare the CNN results with traditional machine learning models and evaluate the results in terms of the accuracy, precision, recall, and F1 scoring techniques. The CBOW CNN with word2vec achieves the best accuracy with 93.41%, significantly improving the accuracy for Tigrinya news classification.

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