An Improved COVID-19 Detection using GAN-Based Data Augmentation and Novel QuNet-Based Classification

Asghar, Usman; Arif, Muhammad; Ejaz, Khurram; Vicoveanu, Dragoş; Izdrui, Diana; Geman, Oana

doi:10.1155/2022/8925930

Cited by 22 publications

(18 citation statements)

References 24 publications

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“…Novel Equation ( 1 ) to Equation ( 4 ) are premap of new region extraction method, and more motivation is there from Algorithm 1 and Algorithm 2. Algorithm 1 and Algorithm 2 are in more explainable fashion in this study by mean of abovementioned equations if we see the one proposed study [ 29 ].…”

Section: Methodssupporting

confidence: 56%

“…is resultant image of the biggest blob sends to the second portion of the algorithm named as a level set algorithm; a set of parameters form a contour. Earlier, this part of the algorithm defines the are in more explainable fashion in this study by mean of abovementioned equations if we see the one proposed study [29].…”

Section: Confidence Region Contour Detection (Crcd)mentioning

confidence: 58%

“…In this referenced study, better tumour detection is found with flavour of SVM classifier for Harvard brain tumour image [27]. In another referenced study, SOM pixel labelling with reduced cluster and deterministic feature clustering give better segmentation [28][29][30][31]. Medical imaging is an important field for identification of disease, so doctor can take better decision in perspective of intelligent computation [32][33][34][35][36][37].…”

Section: Related Workmentioning

confidence: 99%

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Confidence Region Identification and Contour Detection in MRI Image

Ejaz

Arif

Rahim³

et al. 2022

Computational Intelligence and Neuroscience

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Tumour region extraction (RE) method identifies the area of interest in MR imaging as it also highlights tumour boundaries. Some other intensities are existing, they are not visible but have their existence in region, and this region is called growing region. Such region is to be tumour region. Due to the variation of intensities in MRI images, tumour visibility becomes uncleared. Tumour intensity variations (tumour tissues) mix with normal brain tissues. In the light of above circumstance, tumour growing region becomes challenge. The goal of work is to extract the region of interest with confidence. The objective of the study is to develop the region of interest of brain tumour MRI image method by using confidence score for identifying the variation of intensity. The significance of work is based on identification of region of interest (tumour region). Confidence score is measured through pattern of intensities of MRI image. Similar patterns of brain tumour intensities are identified. Each pattern of intensities is adjusted with certain scale, and then biggest blob is analysed. Various biggest area blobs are combined, and resultant biggest blob is formed. In fact, resultant area blob is a combination of different patterns. Each pattern is assigned with particular colour. These colours highlight the growing region. Further, a contour is detected around the tumour boundaries. With combination of region scale fitting and contour detection (CD), tumour boundaries are further separated from normal tissues. Hence, the confidence score (CS) is formed from CD. CS is further converted to confidence region (CR). Conversion to CR is performed though confidence interval (CI). CI is based on defined conditions. In such conditions, different probabilities are considered. Probability identifies the region. Source of region formation is pixels; these pixels highlight tumour core significantly. This CR is obtained through checking standard deviation and statistical evaluation using confidence interval. Hence, region-of-interest pixels are identifying the CR. CR is evaluated through 97% Dice over index (DOI), 94% Jacquard, MSE 1.24, and PSNR 17.45. Value of testing parameter from benchmark study was JI, DOI, and MSE, PSNR : JI was 31.5%, DOI was 47.3%, MSE was 2.5 dB, and PSNR was 40 dB. The parameters are measured for the complex images; contribution parameter classifies the mean pixel values and deviating pixel values, and the classification of the pixel value is like to be termed as intensities. Mentioned classification extracts the variation of intensity pixels accurately; then, algorithm is highlighting the region as compared to the normal tumour cells.

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Section: Methodssupporting

confidence: 56%

Section: Confidence Region Contour Detection (Crcd)mentioning

confidence: 58%

Section: Related Workmentioning

confidence: 99%

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Confidence Region Identification and Contour Detection in MRI Image

Ejaz

Arif

Rahim³

et al. 2022

Computational Intelligence and Neuroscience

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“…In [77], K-means is combined with CNN; in parallel for segmentation, the accuracy with unsupervised learning is more accurate as compared to supervised learning and CNN. In [78][79][80][81][82][83][84], segmentation methods are compared with deep learning. Hence, it is emphasized that not a single study among the referenced ones was capable of solving the issues of variation of intensities, extreme intensities, and features clustering.…”

Section: Related Workmentioning

confidence: 99%

Review on Hybrid Segmentation Methods for Identification of Brain Tumor in MRI

Ejaz

Rahim

Arif

et al. 2022

Contrast Media & Molecular Imaging

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Modalities like MRI give information about organs and highlight diseases. Organ information is visualized in intensities. The segmentation method plays an important role in the identification of the region of interest (ROI). The ROI can be segmented from the image using clustering, features, and region extraction. Segmentation can be performed in steps; firstly, the region is extracted from the image. Secondly, feature extraction performed, and better features are selected. They can be shape, texture, or intensity. Thirdly, clustering segments the shape of tumor, tumor has specified shape, and shape is detected by feature. Clustering consists of FCM, K-means, FKM, and their hybrid. To support the segmentation, we conducted three studies (region extraction, feature, and clustering) which are discussed in the first line of this review paper. All these studies are targeting MRI as a modality. MRI visualization proved to be more accurate for the identification of diseases compared with other modalities. Information of the modality is compromised due to low pass image. In MRI Images, the tumor intensities are variable in tumor areas as well as in tumor boundaries.

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“…Furthermore, it is being discovered that among several inhabitants who have been given pharyngeal swabs, about 50-75% are positive without any symptoms [2] . The solution available to identify the COVID-19 infected person has collected the swab and needs to analyze biological material via polymerase chain reaction [3] . A swab is tested for a patient who shows symptoms.…”

Section: Introductionmentioning

confidence: 99%