An Efficient Deep Convolutional Neural Network for Visual Image Classification

El-Rahiem, Basma Abd; Ahmed, Muhammad Atta Othman; Reyad, Omar; El-Rahaman, Hani Abd; Amin, Mohamed; El‐Samie, Fathi E. Abd

doi:10.1007/978-3-030-14118-9_3

Cited by 15 publications

(4 citation statements)

References 21 publications

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“…There are many edge detection operations are available, which recognizes vertical, level, horizontal, corner, and step edges in an image. The quality, nature of edges identified by these operators is profoundly subject to, noise, clamor, lighting conditions, objects of similar intensities and the density and the thickness of edges in the scene [52]. There are two alternatives which are used for the detection of edges in the image are as follows.…”

Section: Automatic Edge Detection Using Deep Learningmentioning

confidence: 99%

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Solving Image Processing Critical Problems Using Machine Learning

Sharma

Gupta

Jaiswal

2021

Studies in Big Data

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The history of vision can go back around 543 BC. From fossil studies, researchers found out that no. of animal species floats out and soon develops the first animal with eyes. As evolution in bigger species around fifty percent of neurons in our body are involved in the visual processing. Vision allows human to perceive, comprehend, and understand surrounding them while computer vision aims to duplicate the impact of human vision electronically in the form of images. The history of making artificial vison starts around 1545, Gemma Frisius, and Encylopedie in eighteenth century. The foundation period of the concept lies from 1943 to 1956, during this period research is cantered towards understanding the concept of machine learning intelligence, artificial neural system, and automata theory computation. The first work in the field of Artificial intelligence in computer vision and image processing was done in MIT called "Summer Vision Project". The rise of artificial intelligence starts and it is used widely in every field of technology. As the technology advances machine learning is used in almost every field. This book chapter provides you some details how the machine learning is used in image processing and to solve the current problem and challenge in computer vison like: Denoising, Image compression, Image color enhancement, segmentation, etc. Machine learning algorithm like Neural Network, Support Vector Machine, Genetic Algorithm, Convolutional Neural Network, etc. were discussed in the book chapter.

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Section: Automatic Edge Detection Using Deep Learningmentioning

confidence: 99%

“…Convolutional neural network for image classification[52] Image shows the changes of the edge detected output image of the proposed technique; it is obvious that the best…”

mentioning

confidence: 99%

Solving Image Processing Critical Problems Using Machine Learning

Sharma

Gupta

Jaiswal

2021

Studies in Big Data

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“…The suggested model primarily pertains to helping medical experts with the prompt detection and recovery of people infected with COVID-19. The present image classification issue is a challenge that requires supervised learning [11,12]. Supervised learning is a learning technique in which an algorithm is trained on a labelled dataset, which means that the real classes of the samples are already supplied to the model, allowing it to adapt its parameters depending on the training accuracy [13,14].…”

Section: Introductionmentioning

confidence: 99%

Expected Risk Minimization and Robust Preventive Inference of Transfer Learning for COVID-19 Diagnosis within Chest X-Rays

Ahmed¹,

AbdelSattar

Abbas

2023

Sohag Journal of Sciences

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The creation of a treatment strategy and the choice of patient-checking circumstances within many others are supported by early diagnosis of COVID-19 infection. It is possible to detect COVID-19 early on by applying a deep learning method to radiographic medical lab images. Convolutional neural networks (CNN) are used in this study to improve COVID-19 diagnoses using X-ray scans. An automated diagnostic solution that can swiftly deliver accurate diagnostic results is required. CNNs have been found to be efficient at classifying medical images using deep learning techniques. Transfer Learning (TF) is the most reliable research supervised learning method, offering useful analysis to examine many radiographs image samples, and can considerably detect potential and infer preventative detection of COVID-19. Despite its high True Positive, testing healthcare professionals remains a serious risk. Three distinct deep TF and regularization-based architectures were studied on chest X-ray images for the diagnosis of COVID-19. Because these models already include weights trained on the ImageNet database, large training sets are unnecessary. To evaluate the model's performance, 21,165 chest x-ray scan samples were obtained from various sources and identified as COVID-19 data collection from four classes in the Kaggle repository. Average metrics results are collected to get the actual predictions for all classes. Although Saving training time with TF, an advance improvement for performance can be achieved by applying only some parts of the input image with most important segments of the input image are localized. To prove the validity of our approach we use Grad Cam algorithm to find the input image parts with most valuable features for decision making. The localised image region map is udsed to reproduce a lighter version of the image database with only marked as most important image regions. Metrics including precision, F1-Score, confusion matrix, accuracy, sensitivity, specificity, error rate, and error rate have been used to assess the performance of all the TF models., besides false positive (FP), Matthews Correlation Coefficient (MCC), and Kappa performance measures. In terms of performance, the ResNet-50 model outperforms all others with a low error rate of 0.039 and achieves more than a 96% accuracy. The study findings proven the proposed model validity as a computer-aided diagnostics model with a guarantee to supply help for radiologists quickly and accurately.

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“…Various feature extraction methods have been devised to improve efficiency and the ability of classification [4]- [8]. Some work aims to select the most important or active features from raw pixels to efficiently represent the original data [7], and some try to learn a mapping matrix that can transform the high dimensional data to low dimensional subspace [8].…”

Section: Introductionmentioning

confidence: 99%

Automatically Query Active Features Based on Pixel-Level for Facial Expression Recognition

Sun

Zhao

2019

IEEE Access

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Feature extraction-based subspace learning methods normally learn a projection that can convert the high-dimensional data to the low-dimensional representation. However, they may not be suitable for better classification since features obtained by these methods ignore discriminability of the data-pixel itself. Given this, we propose a novel approach that automatically queries active features combing sparse representation classification for the facial expression recognition. The proposed approach aims to automatically query discriminative features from raw pixels, thereby fully considering the underlying characteristics existed in the source data. Especially, the proposed approach based on pixel-level adaptively selects the most active and discriminative feature for representation and classification. The intraclass low-rank decomposition and principal feature analysis are simultaneously used to guarantee that the extracted features can capture the most active energy of the raw data, and thus, the proposed approach can be also applied for other feature extraction and selection tasks. We conduct comprehensive experiments on four public datasets, and the results show superior performance than some state-of-the-art methods.INDEX TERMS Facial expression recognition, automatically query active features, pixel-level, intraclass low-rank matrix, principal feature analysis.

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An Efficient Deep Convolutional Neural Network for Visual Image Classification

Cited by 15 publications

References 21 publications

Solving Image Processing Critical Problems Using Machine Learning

Solving Image Processing Critical Problems Using Machine Learning

Expected Risk Minimization and Robust Preventive Inference of Transfer Learning for COVID-19 Diagnosis within Chest X-Rays

Automatically Query Active Features Based on Pixel-Level for Facial Expression Recognition

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