Hybrid Filter Based on Fuzzy Techniques for Mixed Noise Reduction in Color Images

Arnal, Josep; Súcar, Luis

doi:10.3390/app10010243

Cited by 22 publications

(16 citation statements)

References 39 publications

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“…As a result of this process, a single output variable is passed. The input and output values are determined according to the training data [28,29].…”

Section: Reconstructing Images: Fuzzy Color Techniquementioning

confidence: 99%

COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches

Toğaçar

Ergen

Cömert

2020

Computers in Biology and Medicine

498

360

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Coronavirus causes a wide variety of respiratory infections and it is an RNA-type virus that can infect both humans and animal species. It often causes pneumonia in humans. Artificial intelligence models have been helpful for successful analyses in the biomedical field. In this study, Coronavirus was detected using a deep learning model, which is a sub-branch of artificial intelligence. Our dataset consists of three classes namely: coronavirus, pneumonia, and normal X-ray imagery. In this study, the data classes were restructured using the Fuzzy Color technique as a preprocessing step and the images that were structured with the original images were stacked. In the next step, the stacked dataset was trained with deep learning models (MobileNetV2, SqueezeNet) and the feature sets obtained by the models were processed using the Social Mimic optimization method. Thereafter, efficient features were combined and classified using Support Vector Machines (SVM). The overall classification rate obtained with the proposed approach was 99.27%. With the proposed approach in this study, it is evident that the model can efficiently contribute to the detection of COVID-19 disease.

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“…As a result of this process, a single output variable is passed. The input and output values are determined according to the training data [28,29].…”

Section: Reconstructing Images: Fuzzy Color Techniquementioning

confidence: 99%

COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches

Toğaçar

Ergen

Cömert

2020

Computers in Biology and Medicine

498

360

View full text Add to dashboard Cite

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“…The major problems faced by machine learning-based models are the lack of efficient feature extraction and pre-processing of input images. In [20] , [21] the author showed that pre-processing through the Fuzzy color image enhancement technique can significantly improve feature extraction of computer vision models and hence improve the classification performance.…”

Section: Related Workmentioning

confidence: 99%

InstaCovNet-19: A deep learning classification model for the detection of COVID-19 patients using Chest X-ray

Gupta

Anjum

Gupta

et al. 2021

Applied Soft Computing

197

121

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Recently, the whole world became infected by the newly discovered coronavirus (COVID-19). SARS-CoV-2, or widely known as COVID-19, has proved to be a hazardous virus severely affecting the health of people. It causes respiratory illness, especially in people who already suffer from other diseases. Limited availability of test kits as well as symptoms similar to other diseases such as pneumonia has made this disease deadly, claiming the lives of millions of people. Artificial intelligence models are found to be very successful in the diagnosis of various diseases in the biomedical field In this paper, an integrated stacked deep convolution network InstaCovNet-19 is proposed. The proposed model makes use of various pre-trained models such as ResNet101, Xception, InceptionV3, MobileNet, and NASNet to compensate for a relatively small amount of training data. The proposed model detects COVID-19 and pneumonia by identifying the abnormalities caused by such diseases in Chest X-ray images of the person infected. The proposed model achieves an accuracy of 99.08% on 3 class (COVID-19, Pneumonia, Normal) classification while achieving an accuracy of 99.53% on 2 class (COVID, NON-COVID) classification. The proposed model achieves an average recall, F1 score, and precision of 99%, 99%, and 99%, respectively on ternary classification, while achieving a 100% precision and a recall of 99% on the binary class., while achieving a 100% precision and a recall of 99% on the COVID class. InstaCovNet-19’s ability to detect COVID-19 without any human intervention at an economical cost with high accuracy can benefit humankind greatly in this age of Quarantine.

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“…This greatly reduces the number of data [33]. The enlargement and reduction methods can adjust the range by enlarging or reducing the image when there is a large or small amount of data in the image [34]. The rotation and transformation methods are used when the object to be detected in the image is not in the designated form [35].…”

Section: Image Pre-processing Using Object Detectionmentioning

confidence: 99%

Pothole Classification Model Using Edge Detection in Road Image

Baek

Chung

2020

Applied Sciences

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Since the image related to road damage includes objects such as potholes, cracks, shadows, and lanes, there is a problem that it is difficult to detect a specific object. In this paper, we propose a pothole classification model using edge detection in road image. The proposed method converts RGB (red green and blue) image data, including potholes and other objects, to gray-scale to reduce the amount of computation. It detects all objects except potholes using an object detection algorithm. The detected object is removed, and a pixel value of 255 is assigned to process it as a background. In addition, to extract the characteristics of a pothole, the contour of the pothole is extracted through edge detection. Finally, potholes are detected and classified based by the (you only look once) YOLO algorithm. The performance evaluation evaluates the distortion rate and restoration rate of the image, and the validity of the model and accuracy of the classification. The result of the evaluation shows that the mean square error (MSE) of the distortion rate and restoration rate of the proposed method has errors of 0.2–0.44. The peak signal to noise ratio (PSNR) is evaluated as 50 db or higher. The structural similarity index map (SSIM) is evaluated as 0.71–0.82. In addition, the result of the pothole classification shows that the area under curve (AUC) is evaluated as 0.9.

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Hybrid Filter Based on Fuzzy Techniques for Mixed Noise Reduction in Color Images

Cited by 22 publications

References 39 publications

COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches

COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches

InstaCovNet-19: A deep learning classification model for the detection of COVID-19 patients using Chest X-ray

Pothole Classification Model Using Edge Detection in Road Image

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