Detection Of CT Scan Lungs COVID 19 Image Using Convolutional Neural Network And CLAHE
Detecting COVID 19 is a significant task for medical professionals today because of its rapid spread. To overcome this problem, medical professionals have used various techniques and methods to detect to inhibit the proliferation of COVID 19. CT (Computed Tomography) Scan is currently the best method for detecting COVID 19. This diagnostic method is very accurate because it can see organs in three dimensions. However, this method requires a radiologist to detect the disease and requires a long time, which means it will cut valuable time for medical practitioners if a patient is sick. Therefore it is necessary to implement a system to detect the coronavirus automatically as an alternative quickly. This study intends to help medical practitioners to detect computed tomography (CT) Scans of lungs infected with COVID 19. The methods to be used are Limited Adaptive histogram equalization (CLAHE) contrast to improve the quality of CT (Computed Tomography) Scan images of COVID 19 lungs and Convolutional Neural Network (CNN) for the image classification process. The dataset used is 698 RGB images. This study uses three convolutional layers, 3 Max Pooling layers, and two fully connected layers, resulting in 83.28% accuracy.
2020 3rd International Conference on Information and Communications Technology (ICOIACT)