Investigation of the Effect of Edge Detection Algorithms in the Detection of Covid-19 Patients with Convolutional Neural Network-Based Features on Chest X-Ray Images

Narin, Derya; Onur, Tuğba Özge

doi:10.1109/siu53274.2021.9477882

Cited by 5 publications

(2 citation statements)

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“…This layer processes input images as matrices. Filters determine image attributes in the layer [10]. Images are filtered, and the matrix is multiplied.…”

Section: Bmentioning

confidence: 99%

Deep Convolutional Neural Networks For Classification of Satellite Images

Bihari Nandan Pandey

2023

IJRITCC

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Deep learning algorithms that can learn from image, video, audio, and text data are becoming more successful as hardware power increases. Given the effectiveness and benefits of deep learning in many domains with more data, architecture should see similar implications. This study examined textures using particular rather than overall images. The deep convolutional neural network model classified 4500 satellite photos of clouds, deserts, greenery, and water. The constructed model classified previously unused test data (675 images) with 0.97 accuracies for cloud images, 0.98 for desert images, 0.96 for green areas, and 0.98 for water bodies. Although cloud and desert photos and green and water body images are comparable, this textural success shows that it can detect, analyze, and classify architectural elements. Deep convolutional neural networks can recognize, analyze, and classify architectural materials and elements, enabling shape recognition among many data to help architects collect helpful information. Thus, it will provide more extensive data than manual data analysis, enabling more accurate decisions. Understanding deep convolutional neural network data categorization characteristics explains architectural design differences and similarities. This condition reveals the hidden relationship in designs, allowing architects to create unique designs..

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“…This layer processes input images as matrices. Filters determine image attributes in the layer [10]. Images are filtered, and the matrix is multiplied.…”

Section: Bmentioning

confidence: 99%

Deep Convolutional Neural Networks For Classification of Satellite Images

Bihari Nandan Pandey

2023

IJRITCC

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“…The input image presented to this layer is treated as a matrix. In the layer, the properties of the image are obtained by applying a certain number of filters [10]. Filters are moved over the images and perform matrix multiplications.…”

Section: Convolution Layermentioning

confidence: 99%

Classification of Satellite Images With Deep Convolutional Neural Networks and Its Effect on Architecture

Diker

Erkan

2022

Eskişehir Technical University Journal of Science and Technology a - Applied Sciences and Engineering

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Unlike traditional machine learning methods, deep learning methods that can learn from image, video, audio, and text data, especially recently with the increase in hardware power, are also increasing in success. Considering the success and benefits of deep learning methods in many different fields with increasing data, similar effects are expected in architecture. In this study, we focused on textures by going down to specifics rather than general images. In this direction, a total of 4500 satellite images belonging to cloud, desert, green areas and water bodies were classified in the model developed using deep convolutional neural networks. In the developed model, 0.97 accuracy for cloud images, 0.98 accuracy for desert images, 0.96 accuracy for green areas images and 0.98 accuracy for water bodies images were obtained in the classification of previously unused test data (675 images). Although there are similarities in the images of cloud and desert, and images of green areas and water bodies, this success in textures shows that it can be successful in detecting, analyzing, and classifying architectural materials. Successful recognition, analysis and classification of architectural materials and elements with deep convolutional neural networks will be able to facilitate the acquisition of appropriate and useful data through shape recognition among many data, especially at the information collection phase in the architectural design process. Thus, it will help to take more accurate decisions by obtaining more comprehensive data that cannot be obtained from manual data analysis. Learning the distinctive features for classification of data in deep convolutional neural networks also explains architectural design differences and similarities. This situation reveals the hidden relationship in the designs and thus can offer architects the opportunity to make creative and original designs.

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