Higher Resolution Input Image of Convolutional Neural Network of Reinforced Concrete Earthquake-Generated Crack Classification and Localization

AbstractLeukaemia detection and diagnosis in advance is the trending topic in the medical applications for reducing the death toll of patients with acute lymphoblastic leukaemia (ALL). For the detection of ALL, it is essential to analyse the white blood cells (WBCs) for which the blood smear images are employed. This paper proposes a new technique for the segmentation and classification of the acute lymphoblastic leukaemia. The proposed method of automatic leukaemia detection is based on the Deep Convolutional Neural Network (Deep CNN) that is trained using an optimization algorithm, named Grey wolf-based Jaya Optimization Algorithm (GreyJOA), which is developed using the Grey Wolf Optimizer (GWO) and Jaya Optimization Algorithm (JOA) that improves the global convergence. Initially, the input image is applied to pre-processing and the segmentation is performed using the Sparse Fuzzy C-Means (Sparse FCM) clustering algorithm. Then, the features, such as Local Directional Patterns (LDP) and colour histogram-based features, are extracted from the segments of the pre-processed input image. Finally, the extracted features are applied to the Deep CNN for the classification. The experimentation evaluation of the method using the images of the ALL IDB2 database reveals that the proposed method acquired a maximal accuracy, sensitivity, and specificity of 0.9350, 0.9528, and 0.9389, respectively.

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Research on the Extraction of Image Edge Information in Convolutional Neural Networks

Journal of Physics Conference Series ◽

10.1088/1742-6596/2083/3/032015 ◽

2021 ◽

Vol 2083 (3) ◽

pp. 032015

Author(s):

Guanru Zou ◽

Yulin Luo ◽

Zefeng Feng

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Input Image ◽

Edge Weight ◽

Edge Information ◽

Image Dimension ◽

Original Dataset ◽

Image Edge ◽

Accuracy Rates ◽

Object Move

Abstract Convolutional neural network is an important neural network model in deep learning and a common algorithm in computer vision problems. From the perspective of practical application scenarios, this paper studies whether padding in convolutional neural network convolution layer weakens the image edge information. In order to eliminate the background factor, this paper select MNIST dataset as the research object, move the 0-9 digital image to the specified image edge by clearing the white area pixels in the specified direction, and use OpenCV to realize bilinear interpolation to scale the image to ensure that the image dimension is 28×28. The convolution neural network is built to train the original dataset and the processed dataset, and the accuracy rates are 0.9892 and 0.1082 respectively. In the comparative experiment, padding cannot solve the problem of weakening the image edge weight well. In the actual digital recognition scene, it is necessary to consider whether the core recognition area in the input image is at the edge of the image.

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Pavement Crack Detection and Segmentation Method Based on Improved Deep Learning Fusion Model

Mathematical Problems in Engineering ◽

10.1155/2020/8515213 ◽

2020 ◽

Vol 2020 ◽

pp. 1-22

Author(s):

Xiaoran Feng ◽

Liyang Xiao ◽

Wei Li ◽

Lili Pei ◽

Zhaoyun Sun ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Geometric Parameter ◽

Crack Detection ◽

Crack Identification ◽

Single Shot ◽

Road Maintenance ◽

Fusion Model ◽

Detection Model ◽

Crack Classification

Pavement damage is the main factor affecting road performance. Pavement cracking, a common type of road damage, is a key challenge in road maintenance. In order to achieve an accurate crack classification, segmentation, and geometric parameter calculation, this paper proposes a method based on a deep convolutional neural network fusion model for pavement crack identification, which combines the advantages of the multitarget single-shot multibox detector (SSD) convolutional neural network model and the U-Net model. First, the crack classification and detection model is applied to classify the cracks and obtain the detection confidence. Next, the crack segmentation network is applied to accurately segment the pavement cracks. By improving the feature extraction structure and optimizing the hyperparameters of the model, pavement crack classification and segmentation accuracy were improved. Finally, the length and width (for linear cracks) and the area (for alligator cracks) are calculated according to the segmentation results. Test results show that the recognition accuracy of the pavement crack identification method for transverse, longitudinal, and alligator cracks is 86.8%, 87.6%, and 85.5%, respectively. It is demonstrated that the proposed method can provide the category information for pavement cracks as well as the accurate positioning and geometric parameter information, which can be used directly for evaluating the pavement condition.

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Visual Interpretation of Convolutional Neural Network Predictions in Classifying Medical Image Modalities

Diagnostics ◽

10.3390/diagnostics9020038 ◽

2019 ◽

Vol 9 (2) ◽

pp. 38 ◽

Cited By ~ 10

Author(s):

Incheol Kim ◽

Sivaramakrishnan Rajaraman ◽

Sameer Antani

Keyword(s):

Neural Network ◽

Information Retrieval ◽

Convolutional Neural Network ◽

Visual Information ◽

Medical Image ◽

Input Image ◽

Visual Interpretation ◽

Feature Maps ◽

Novel Method ◽

Series Of Experiments

Deep learning (DL) methods are increasingly being applied for developing reliable computer-aided detection (CADe), diagnosis (CADx), and information retrieval algorithms. However, challenges in interpreting and explaining the learned behavior of the DL models hinders their adoption and use in real-world systems. In this study, we propose a novel method called “Class-selective Relevance Mapping” (CRM) for localizing and visualizing discriminative regions of interest (ROI) within a medical image. Such visualizations offer improved explanation of the convolutional neural network (CNN)-based DL model predictions. We demonstrate CRM effectiveness in classifying medical imaging modalities toward automatically labeling them for visual information retrieval applications. The CRM is based on linear sum of incremental mean squared errors (MSE) calculated at the output layer of the CNN model. It measures both positive and negative contributions of each spatial element in the feature maps produced from the last convolution layer leading to correct classification of an input image. A series of experiments on a “multi-modality” CNN model designed for classifying seven different types of image modalities shows that the proposed method is significantly better in detecting and localizing the discriminative ROIs than other state of the art class-activation methods. Further, to visualize its effectiveness we generate “class-specific” ROI maps by averaging the CRM scores of images in each modality class, and characterize the visual explanation through their different size, shape, and location for our multi-modality CNN model that achieved over 98% performance on a dataset constructed from publicly available images.

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Freehand Sketch-Based Authenticated Security System using Convolutional Neural Network

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.b4412.129219 ◽

2019 ◽

Vol 9 (2) ◽

pp. 3405-3411

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Gaussian Mixture ◽

Adaptive Threshold ◽

Input Image ◽

Security System ◽

Security Strategy ◽

System Database ◽

Freehand Sketch ◽

Authentication Security

An Authenticated Security System is a highly desired feature. In this paper, a FreeHand Sketch-based Authentication Security strategy is proposed for authentication purposes by allowing a user to choose one label from a collection of different labels and asking him to sketch the corresponding image for the selected label for registration to avoid mischievous registration and the sketched image gets preprocessed using adaptive threshold with Gaussian mixture and then predicted with a trained Convolutional Neural Network(CNN) data model to generate the necessary image label. The produced image label will compare with selected image label. If both are same then the details will store in the system database. The user gets login with his/her authorized details with sketch based image password. The image password gets preprocessed using adaptive threshold with Gaussian mixture and then predicted with a trained CNN model to produce the image name. The produced image name will compare with the system database for authentication. The methodology is tested with some sample input image passwords and the performance calculation is carried out using metrics like Recall and Precision. The proposed work exhibits the accuracy of approximately 85% by ensuring the authentication for the user security.

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Introspective analysis of convolutional neural networks for improving discrimination performance and feature visualisation

PeerJ Computer Science ◽

10.7717/peerj-cs.497 ◽

2021 ◽

Vol 7 ◽

pp. e497

Author(s):

Shakeel Shafiq ◽

Tayyaba Azim

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Network ◽

Convolutional Neural Networks ◽

Discrimination Performance ◽

Input Image ◽

Data Sets ◽

Discrimination Power ◽

Level Information ◽

Fully Connected

Deep neural networks have been widely explored and utilised as a useful tool for feature extraction in computer vision and machine learning. It is often observed that the last fully connected (FC) layers of convolutional neural network possess higher discrimination power as compared to the convolutional and maxpooling layers whose goal is to preserve local and low-level information of the input image and down sample it to avoid overfitting. Inspired from the functionality of local binary pattern (LBP) operator, this paper proposes to induce discrimination into the mid layers of convolutional neural network by introducing a discriminatively boosted alternative to pooling (DBAP) layer that has shown to serve as a favourable replacement of early maxpooling layer in a convolutional neural network (CNN). A thorough research of the related works show that the proposed change in the neural architecture is novel and has not been proposed before to bring enhanced discrimination and feature visualisation power achieved from the mid layer features. The empirical results reveal that the introduction of DBAP layer in popular neural architectures such as AlexNet and LeNet produces competitive classification results in comparison to their baseline models as well as other ultra-deep models on several benchmark data sets. In addition, better visualisation of intermediate features can allow one to seek understanding and interpretation of black box behaviour of convolutional neural networks, used widely by the research community.

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Kinerja Metode CNN untuk Klasifikasi Pneumonia dengan Variasi Ukuran Citra Input

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2021834515 ◽

2021 ◽

Vol 8 (3) ◽

pp. 533

Author(s):

Budi Nugroho ◽

Eva Yulia Puspaningrum

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Extreme Learning Machine ◽

Classification Performance ◽

Input Image ◽

X Rays ◽

Image Size ◽

X Ray ◽

The Difference ◽

Learning Machine

Saat ini banyak dikembangkan proses pendeteksian pneumonia berdasarkan citra paru-paru dari hasil foto rontgen (x-ray), sebagaimana juga dilakukan pada penelitian ini. Metode yang digunakan adalah Convolutional Neural Network (CNN) dengan arsitektur yang berbeda dengan sejumlah penelitian sebelumnya. Selain itu, penelitian ini juga memodifikasi model CNN dimana metode Extreme Learning Machine (ELM) digunakan pada bagian klasifikasi, yang kemudian disebut CNN-ELM. Dataset untuk uji coba menggunakan kumpulan citra paru-paru hasil foto rontgen pada Kaggle yang terdiri atas 1.583 citra normal dan 4.237 citra pneumonia. Citra asal pada dataset kaggle ini bervariasi, tetapi hampir semua diatas ukuran 1000x1000 piksel. Ukuran citra yang besar ini dapat membuat pemrosesan klasifikasi kurang efektif, sehingga mesin CNN biasanya memodifikasi ukuran citra menjadi lebih kecil. Pada penelitian ini, pengujian dilakukan dengan variasi ukuran citra input, untuk mengetahui pengaruhnya terhadap kinerja mesin pengklasifikasi. Hasil uji coba menunjukkan bahwa ukuran citra input berpengaruh besar terhadap kinerja klasifikasi pneumonia, baik klasifikasi yang menggunakan metode CNN maupun CNN-ELM. Pada ukuran citra input 200x200, metode CNN dan CNN-ELM menunjukkan kinerja paling tinggi. Jika kinerja kedua metode itu dibandingkan, maka Metode CNN-ELM menunjukkan kinerja yang lebih baik daripada CNN pada semua skenario uji coba. Pada kondisi kinerja paling tinggi, selisih akurasi antara metode CNN-ELM dan CNN mencapai 8,81% dan selisih F1 Score mencapai 0,0729. Hasil penelitian ini memberikan informasi penting bahwa ukuran citra input memiliki pengaruh besar terhadap kinerja klasifikasi pneumonia, baik klasifikasi menggunakan metode CNN maupun CNN-ELM. Selain itu, pada semua ukuran citra input yang digunakan untuk proses klasifikasi, metode CNN-ELM menunjukkan kinerja yang lebih baik daripada metode CNN. AbstractThis research developed a pneumonia detection machine based on the lungs' images from X-rays (x-rays). The method used is the Convolutional Neural Network (CNN) with a different architecture from some previous research. Also, the CNN model is modified, where the classification process uses the Extreme Learning Machine (ELM), which is then called the CNN-ELM method. The empirical experiments dataset used a collection of lung x-ray images on Kaggle consisting of 1,583 normal images and 4,237 pneumonia images. The original image's size on the Kaggle dataset varies, but almost all of the images are more than 1000x1000 pixels. For classification processing to be more effective, CNN machines usually use reduced-size images. In this research, experiments were carried out with various input image sizes to determine the effect on the classifier's performance. The experimental results show that the input images' size has a significant effect on the classification performance of pneumonia, both the CNN and CNN-ELM classification methods. At the 200x200 input image size, the CNN and CNN-ELM methods showed the highest performance. If the two methods' performance is compared, then the CNN-ELM Method shows better performance than CNN in all test scenarios. The difference in accuracy between the CNN-ELM and CNN methods reaches 8.81% at the highest performance conditions, and the difference in F1-Score reaches 0.0729. This research provides important information that the size of the input image has a major influence on the classification performance of pneumonia, both classification using the CNN and CNN-ELM methods. Also, on all input image sizes used for the classification process, the CNN-ELM method shows better performance than the CNN method.

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Super-resolution Ultrasound Imaging Scheme Based on a Symmetric Series Convolutional Neural Network

10.36227/techrxiv.15066315 ◽

2021 ◽

Author(s):

Lakpa Dorje Tamang

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Convolutional Neural Network ◽

Ultrasound Image ◽

Similarity Index ◽

Super Resolution ◽

Input Image ◽

Feature Maps ◽

Proposed Model ◽

Reconstruction Performance

In this paper, we propose a symmetric series convolutional neural network (SS-CNN), which is a novel deep convolutional neural network (DCNN)-based super-resolution (SR) technique for ultrasound medical imaging. The proposed model comprises two parts: a feature extraction network (FEN) and an up-sampling layer. In the FEN, the low-resolution (LR) counterpart of the ultrasound image passes through a symmetric series of two different DCNNs. The low-level feature maps obtained from the subsequent layers of both DCNNs are concatenated in a feed forward manner, aiding in robust feature extraction to ensure high reconstruction quality. Subsequently, the final concatenated features serve as an input map to the latter 2D convolutional layers, where the textural information of the input image is connected via skip connections. The second part of the proposed model is a sub-pixel convolutional (SPC) layer, which up-samples the output of the FEN by multiplying it with a multi-dimensional kernel followed by a periodic shuffling operation to reconstruct a high-quality SR ultrasound image. We validate the performance of the SS-CNN with publicly available ultrasound image datasets. Experimental results show that the proposed model achieves an exquisite reconstruction performance of ultrasound image over the conventional methods in terms of peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM), while providing compelling SR reconstruction time.

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Depth Estimation and Semantic Segmentation from a Single RGB Image Using a Hybrid Convolutional Neural Network

Sensors ◽

10.3390/s19081795 ◽

2019 ◽

Vol 19 (8) ◽

pp. 1795 ◽

Cited By ~ 5

Author(s):

Xiao Lin ◽

Dalila Sánchez-Escobedo ◽

Josep R. Casas ◽

Montse Pardàs

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

State Of The Art ◽

Semantic Segmentation ◽

Depth Estimation ◽

Input Image ◽

Estimation Accuracy ◽

Single Task ◽

Qualitative And Quantitative ◽

Highly Correlated

Semantic segmentation and depth estimation are two important tasks in computer vision, and many methods have been developed to tackle them. Commonly these two tasks are addressed independently, but recently the idea of merging these two problems into a sole framework has been studied under the assumption that integrating two highly correlated tasks may benefit each other to improve the estimation accuracy. In this paper, depth estimation and semantic segmentation are jointly addressed using a single RGB input image under a unified convolutional neural network. We analyze two different architectures to evaluate which features are more relevant when shared by the two tasks and which features should be kept separated to achieve a mutual improvement. Likewise, our approaches are evaluated under two different scenarios designed to review our results versus single-task and multi-task methods. Qualitative and quantitative experiments demonstrate that the performance of our methodology outperforms the state of the art on single-task approaches, while obtaining competitive results compared with other multi-task methods.

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Extracting Crop Spatial Distribution from Gaofen 2 Imagery Using a Convolutional Neural Network

Applied Sciences ◽

10.3390/app9142917 ◽

2019 ◽

Vol 9 (14) ◽

pp. 2917 ◽

Cited By ~ 3

Author(s):

Yan Chen ◽

Chengming Zhang ◽

Shouyi Wang ◽

Jianping Li ◽

Feng Li ◽

...

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Spatial Distribution ◽

Convolutional Neural Network ◽

Spectral Feature ◽

Input Image ◽

Remote Sensing Images ◽

Feature Map ◽

Feature Extractor ◽

Extraction Unit

Using satellite remote sensing has become a mainstream approach for extracting crop spatial distribution. Making edges finer is a challenge, while simultaneously extracting crop spatial distribution information from high-resolution remote sensing images using a convolutional neural network (CNN). Based on the characteristics of the crop area in the Gaofen 2 (GF-2) images, this paper proposes an improved CNN to extract fine crop areas. The CNN comprises a feature extractor and a classifier. The feature extractor employs a spectral feature extraction unit to generate spectral features, and five coding-decoding-pair units to generate five level features. A linear model is used to fuse features of different levels, and the fusion results are up-sampled to obtain a feature map consistent with the structure of the input image. This feature map is used by the classifier to perform pixel-by-pixel classification. In this study, the SegNet and RefineNet models and 21 GF-2 images of Feicheng County, Shandong Province, China, were chosen for comparison experiment. Our approach had an accuracy of 93.26%, which is higher than those of the existing SegNet (78.12%) and RefineNet (86.54%) models. This demonstrates the superiority of the proposed method in extracting crop spatial distribution information from GF-2 remote sensing images.

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