Fused Deep Convolutional Neural Network for Precision Diagnosis of COVID-19 Using Chest X-Ray Images
With a Coronavirus disease (COVID-19) case count exceeding 10 million worldwide, there is an increased need for a diagnostic capability. The main variables in increasing diagnostic capability are reduced cost, turnaround or diagnosis time, and upfront equipment cost and accessibility. Two candidates for machine learning COVID-19 diagnosis are Computed Tomography (CT) scans and plain chest X-rays. While CT scans score higher in sensitivity, they have a higher cost, maintenance requirement, and turnaround time as compared to plain chest X-rays. The use of portable chest X-radiograph (CXR) is recommended by the American College of Radiology (ACR) since using CT places a massive burden on radiology services. Therefore, X-ray imagery paired with machine learning techniques is proposed a first-line triage tool for COVID-19 diagnostics. In this paper we propose a computer-aided diagnosis (CAD) to accurately classify chest X-ray scans of COVID-19 and normal subjects by fine-tuning several neural networks (ResNet18, ResNet50, DenseNet201) pre-trained on the ImageNet dataset. These neural networks are fused in a parallel architecture and the voting criteria are applied in the final classification decision between the candidate object classes where the output of each neural network is representing a single vote. Several experiments are conducted on the weakly labeled COVID-19-CT-CXR dataset consisting of 263 COVID-19 CXR images extracted from PubMed Central Open Access subsets combined with 25 normal classification CXR images. These experiments show an optimistic result and a capability of the proposed model to outperforming many state-of-the-art algorithms on several measures. Using k-fold cross-validation and a bagging classifier ensemble, we achieve an accuracy of 99.7% and a sensitivity of 100%.
Code (0)
등록된 구현이 없습니다.
Tasks
Computed Tomography (CT)COVID-19 DiagnosisDiagnosticMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Towards Clinical Practice: Design and Implementation of Convolutional Neural Network-Based Assistive Diagnosis System for COVID-19 Case Detection from Chest X-Ray Images
One of the critical tools for early detection and subsequent evaluation of the incidence of lung diseases is chest radiography. This study presents a real-world implementation of a convolutional neural network (CNN) base…
Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network
Chest X-ray is the first imaging technique that plays an important role in the diagnosis of COVID-19 disease. Due to the high availability of large-scale annotated image datasets, great success has been achieved using co…
General ClassificationMedical DiagnosisObject RecognitionSpecificity+1Efficient and Visualizable Convolutional Neural Networks for COVID-19 Classification Using Chest CT
With COVID-19 cases rising rapidly, deep learning has emerged as a promising diagnosis technique. However, identifying the most accurate models to characterize COVID-19 patients is challenging because comparing results o…
COVID-19 DiagnosisDiagnosticGeneral ClassificationSensitivity+1A Novel and Reliable Deep Learning Web-Based Tool to Detect COVID-19 Infection from Chest CT-Scan
The corona virus is already spread around the world in many countries, and it has taken many lives. Furthermore, the world health organization (WHO) has announced that COVID-19 has reached the global epidemic stage. Earl…
Recognition of COVID-19 Disease Utilizing X-Ray Imaging of the Chest Using CNN
Since this COVID-19 pandemic thrives, the utilization of X-Ray images of the Chest (CXR) as a complementary screening technique to RT-PCR testing grows to its clinical use for respiratory complaints. Many new deep learni…