Automatic Lung Disease Detection and Diagnosis Using Optimized Fuzzy Filter and Deep Learning Method
Senthil Pandi S, S Kanimozhi, Rahul Chiranjeevi, M Ishwarya
Abstract
Corona Virus Disease (COVID-19) is a life-threatening disease that was found in December of 2019 in Wuhan, China. A quick prediction can isolate infected people and prevent the disease from spreading to others. For some people, the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test yields the negative result, but they may have an infection, so CT image prediction is the best solution for patients with symptoms. The primary goal of this research is to detect lungs infection quickly and automatically, as well as to improve COVID-19 prediction accuracy by improving image quality through image restoration and enhancement. The proposed research is divided into two modules: the first explains how to improve the quality of input lungs Computerized Tomography (CT) images using an optimized fuzzy filter, and the second explains how to predict and diagnose COVID-19 using DenseNet-121. The investigational results show that the proposed approach can accomplish 0.96 AUC score, 0.88 precision, 0.92 recall, 0.90 accuracy and 0.259 model loss. The proposed method outperforms previous works in CT images. Experimental results indicate that introducing an optimized fuzzy filter, which is used for image restoration and enhancement, improves prediction accuracy.