Deep Convolution Neural Network for Respiratory Diseases Detection Using Radiology Images
Keywords:
Respiratory disease, COVID-19 detection, Radiology images, CT & X-ray images, DNNAbstract
The respiratory issue has placed enormous and increasing pressure on the world's medical systems. The diagnosis, treatment, & even early identification of diseases may all be aided by processing performed on medical images. Deep Learning(DL) models would help physicians and radiologists provide patients with quick diagnostic support. Our research focuses on respiratory diseases like COVID-19, and pneumonia case identification model based on DL. It is trained using a dataset comprised of Chest X-ray (CXR) & CT (Computed Tomography) scan images. The major contribution of our research is to improve image dataset balancing, preprocessing radiology pictures to locate regions of interest (ROI), and further custom-built 28-layered Deep Neural Network as its architecture for deep learning. The dataset used to train the model was gathered from various publicly available time datasets collected from local hospitals. CT scan image data comprised 2 classes: COVID-19 (+) and COVID-19 (-), while X-ray image data included Multi Class(3 class labels): COVID-19, normal, and pneumonia cases. Using CXR pictures, this model trained with 99.12% accuracy for binary classes and 99.94% accuracy for Multiclass cases. The proposed model can attain a validation accuracy of 95.17% for Binary-class cases and 93.97% for multi-class cases. Improvising preprocessing to detect Region of Interest(ROI) and a high level of accuracy is a novel and potentially significant resource that enables radiologists to promptly detect and diagnose respiratory diseases using machine learning approaches even at the early stage of diagnosis.
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