In the proposed research work; the COVID-19 is detected using transfer learning from CT scan images decomposed to three-level using stationary wavelet. A three-phase detection model is proposed to improve the detection accuracy and the procedures are as follows; Phase1- data augmentation using stationary wavelets, Phase2- COVID-19 detection using pre-trained CNN model and Phase3- abnormality localization in CT scan images. This work has considered the well known pre-trained architectures, such as ResNet18, ResNet50, ResNet101, and SqueezeNet for the experimental evaluation. In this work, 70% of images are considered to train the network and 30% images are considered to validate the network. The performance of the considered architectures is evaluated by computing the common performance measures.<br><br>
Infection/disease in lung is one of the acute illnesses in humans. Pneumonia is one of the major lung diseases and each year; the death rate due to the untreated pneumonia is on rise globally. From December 2019; the pneumonia caused by the Coronavirus Disease (COVID-19) has emerged as a global threat due to its rapidity. The clinical level assessment of the COVID-19 is normally performed with the Computed-Tomography scan Slice (CTS) or the Chest X-ray. This research aims to propose an image processing system to examine the COVID-19 infection in CTS. This work implements Cuckoo-Search-Algorithm (CSA) monitored Kapur/Otsu image thresholding and a chosen image segmentation procedure to extract the pneumonia infection. After extracting the COVID-19 infection from the CTS, a relative assessment is then executed with the Ground-Truth-Image (GTI) offered by a radiologist and the essential performance measures are then computed to confirm the superiority of the proposed technique. This work also presents a comparative assessment among the segmentation procedures, such as Level-Set (LS) and Chan-Vese (CV) methods. The experimental outcome authenticates that, the results by Kapur and Otsu threshold are approximately similar when the LS is implemented and the CV with the Otsu presents better values of Jaccard, Dice and Accuracy compared to other methods presented in this research.
In the proposed research work; the COVID-19 is detected using transfer learning from CT scan images decomposed to three-level using stationary wavelet. A three-phase detection model is proposed to improve the detection accuracy and the procedures are as follows; Phase1- data augmentation using stationary wavelets, Phase2- COVID-19 detection using pre-trained CNN model and Phase3- abnormality localization in CT scan images. This work has considered the well known pre-trained architectures, such as ResNet18, ResNet50, ResNet101, and SqueezeNet for the experimental evaluation. In this work, 70% of images are considered to train the network and 30% images are considered to validate the network. The performance of the considered architectures is evaluated by computing the common performance measures.<br><br>
IntroductionCancer happening rates in humankind are gradually rising due to a variety of reasons, and sensible detection and management are essential to decrease the disease rates. The kidney is one of the vital organs in human physiology, and cancer in the kidney is a medical emergency and needs accurate diagnosis and well-organized management.MethodsThe proposed work aims to develop a framework to classify renal computed tomography (CT) images into healthy/cancer classes using pre-trained deep-learning schemes. To improve the detection accuracy, this work suggests a threshold filter-based pre-processing scheme, which helps in removing the artefact in the CT slices to achieve better detection. The various stages of this scheme involve: (i) Image collection, resizing, and artefact removal, (ii) Deep features extraction, (iii) Feature reduction and fusion, and (iv) Binary classification using five-fold cross-validation.Results and discussionThis experimental investigation is executed separately for: (i) CT slices with the artefact and (ii) CT slices without the artefact. As a result of the experimental outcome of this study, the K-Nearest Neighbor (KNN) classifier is able to achieve 100% detection accuracy by using the pre-processed CT slices. Therefore, this scheme can be considered for the purpose of examining clinical grade renal CT images, as it is clinically significant.
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