ArticleMicroscopy research and technique2022
Microscopic segmentation and classification of COVID-19 infection with ensemble convolutional neural network.
Article in Microscopy research and technique, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- DDiagnostics (Basel, Switzerland) · 2022Article
- Three-Dimensional Semantic Segmentation of Diabetic Retinopathy Lesions and Grading Using Transfer Learning.Journal of personalized medicine · 2022Article
- Recognition of Knee Osteoarthritis (KOA) Using YOLOv2 and Classification Based on Convolutional Neural Network.Life (Basel, Switzerland) · 2022Article
- COVID-DAI: A novel framework for COVID-19 detection and infection growth estimation using computed tomography images.Microscopy research and technique · 2022Article
- RETRACTED: Liver Tumor Localization Based on YOLOv3 and 3D-Semantic Segmentation Using Deep Neural Networks.Diagnostics (Basel, Switzerland) · 2022Article
- Microscopic segmentation and classification of COVID-19 infection with ensemble convolutional neural network.Microscopy research and technique · 2022Article
- Computed Tomography Image Features under Denoising Algorithm for Benign and Malignant Diagnosis of Renal Parenchymal Tumor.Contrast media & molecular imaging · 2022Article
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Authors and funding
6 authors.
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Abstract
The detection of biological RNA from sputum has a comparatively poor positive rate in the initial/early stages of discovering COVID-19, as per the World Health Organization. It has a different morphological structure as compared to healthy images, manifested by computer tomography (CT). COVID-19 diagnosis at an early stage can aid in the timely cure of patients, lowering the mortality rate. In this reported research, three-phase model is proposed for COVID-19 detection. In Phase I, noise is removed from CT images using a denoise convolutional neural network (DnCNN). In the Phase II, the actual lesion region is segmented from the enhanced CT images by using deeplabv3 and ResNet-18. In Phase III, segmented images are passed to the stack sparse autoencoder (SSAE) deep learning model having two stack auto-encoders (SAE) with the selected hidden layers. The designed SSAE model is based on both SAE and softmax layers for COVID19 classification. The proposed method is evaluated on actual patient data of Pakistan Ordinance Factories and other public benchmark data sets with different scanners/mediums. The proposed method achieved global segmentation accuracy of 0.96 and 0.97 for classification.
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