Abstract:Corona Virus Disease-2019 (COVID-19) continues to spread rapidly in the world. It has dramatically affected daily lives, public health, and the world economy. This paper presents a segmentation and classification framework of COVID-19 images based on deep learning. Firstly, the classification process is employed to discriminate between COVID-19, non-COVID, and pneumonia by Convolutional Neural Network (CNN). Then, the segmentation process is applied for COVID-19 and pneumonia CT images. Finally, the resulting… Show more
“… 2021 ) ResNet50+SVM X-ray 99.64 Segmentation (El-Shafai et al. 2021a ) DCNN CT 99.56 SIGTra (El-Shafai et al. 2021b ) SR-GAN+TCNN X-ray 99 Proposed work Histogram minimum, histogram maximum, kurtosis, skwness and std+GP X-ray 97.53 Deep feature with InceptionResnet-v2 + GP 100 TL-based with InceptionResnet-v2 100 CNN model built from scratch 94.78 Fig.…”
Section: Discussion and Comparison With The-state-of-the-art Methodsmentioning
confidence: 99%
“…El-Shafai et al. ( 2021a ) introduced an algorithm for segmentation and classification of COVID-19 images based on DL. Firstly, the classification process is employed to differentiate between COVID-19 and pneumonia images with the CNN model.…”
“… 2021 ) ResNet50+SVM X-ray 99.64 Segmentation (El-Shafai et al. 2021a ) DCNN CT 99.56 SIGTra (El-Shafai et al. 2021b ) SR-GAN+TCNN X-ray 99 Proposed work Histogram minimum, histogram maximum, kurtosis, skwness and std+GP X-ray 97.53 Deep feature with InceptionResnet-v2 + GP 100 TL-based with InceptionResnet-v2 100 CNN model built from scratch 94.78 Fig.…”
Section: Discussion and Comparison With The-state-of-the-art Methodsmentioning
confidence: 99%
“…El-Shafai et al. ( 2021a ) introduced an algorithm for segmentation and classification of COVID-19 images based on DL. Firstly, the classification process is employed to differentiate between COVID-19 and pneumonia images with the CNN model.…”
“…The encryption key is used to encrypt image chunks that have been determined to contain different features. This encryption process transforms the image data into ciphertext form, and without the corresponding DecKey, one cannot obtain the original data, thus protecting the privacy of the image data [33]. The DecKey is used to decrypt these encrypted chunks.…”
Image feature categorization has emerged as a crucial component in many domains, including computer vision, machine learning, and biometrics, in the dynamic environment of big data and cloud computing. It is extremely difficult to guarantee image data security, privacy, and computing efficiency while also lowering storage and transmission costs. This paper introduces a novel method for classifying image features that combines multilevel homomorphic encryption and image data partitioning in an integrated manner. We employ a novel partitioning strategy to reduce computational complexity, significantly reducing computational load and improving classification accuracy. In the quest for increased data security and privacy, we introduce a novel, fully homomorphic encryption approach specialized to partitioned images. To counter the inherent complexity of encryption, we devise a compound encryption strategy that exploits the full potential of homomorphic computation, with an explicit objective to curtail computational and storage overheads. Evidently superior to conventional methods, our methodology showcases pronounced benefits in computational efficiency, storage and transmission cost reduction, and robust security and privacy preservation. Hence, the methodology put forth in this paper presents a pioneering and efficacious resolution to the multifaceted challenges of image feature classification within the intricate milieu of cloud computing and big data.
“…Convolutional layer. The convolutional layer is the important layer of CNN that uses convolution operations to extract the features from the input images [ 6 ]. In the proposed models, each convolutional layer has kernel of size 3 × 3.…”
Section: Proposed Frameworkmentioning
confidence: 99%
“…The CT scan is a procedure that uses X-rays and a computer to produce cross-sectional images of various structures inside of the human body. The CT scan can assist a doctor to determine the position, form and size of a tumor before making the radiotherapy [ 6 ]. CXR, in particular, is a widely used, relatively inexpensive, quick, and accessible diagnostic modality that can be quickly carried to the patient’s bedside, even in emergency rooms.…”
The Covid-19 was first appeared in 2019 in Wuhan, China. It widely and rapidly expanded all over the world. Since then, it has had a strong effect on people’s daily lives, the world economy and the public health. The fast prediction of Covid-19 can assist the medicine to choose the right treatment. In this paper, we propose a classification of Covid-19 using Models based on a Convolutional Neural Network (CNN). We propose two models to detect Covid-19. The first one uses CNN with CT or X-ray images separately. The second uses CNN with both CT and X-ray images at the same time. The used datasets contain X-ray and CT images divided into three classes which are Covid-19, Normal and Pneumonia. Each type image class has 1045 images for training and 300 for testing. All these data sets are available in Kaggle repository. In order to evaluate the proposed models, we calculate the confusion matrix, the accuracy, precision, recall and F1 score. The model that uses CNN with both X-ray and CT images of 0.99 achieves the best accuracy. We deduced that using CT images is more efficient than using X-ray images to predict Covid-19. The combination of the CT and X-ray images to detect Covid-19 is more efficient than using only CT or X-ray images. The proposed models could effectively assist the radiologists in predicting Covid-19.
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