Abstract
Coronavirus disease-2019 (COVID-19) is a new types of coronavirus which have turned into a pandemic within a short time. Reverse transcription-polymerase chain reaction (RT-PCR) test is used for the diagnosis of COVID-19 in national healthcare centers. Because the number of PCR test kits is often limited, it is sometimes difficult to diagnose the disease at an early stage. However, X-ray technology is accessible nearly all over the world, and it succeeds in detecting symptoms of COVID-19 more successfully. Another disease which affects people’s lives to a great extent is colorectal cancer. Tissue microarray (TMA) is a technological method which is widely used for its high performance in the analysis of colorectal cancer. Computer-assisted approaches which can classify colorectal cancer in TMA images are also needed. In this respect, the present study proposes a convolutional neural network (CNN) classification approach with optimized parameters using gradient-based optimizer (GBO) algorithm. Thanks to the proposed approach, COVID-19, normal, and viral pneumonia in various chest X-ray images can be classified accurately. Additionally, other types such as epithelial and stromal regions in epidermal growth factor receptor (EFGR) colon in TMAs can also be classified. The proposed approach was called COVID-CCD-Net. AlexNet, DarkNet-19, Inception-v3, MobileNet, ResNet-18, and ShuffleNet architectures were used in COVID-CCD-Net, and the hyperparameters of this architecture was optimized for the proposed approach. Two different medical image classification datasets, namely, COVID-19 and Epistroma, were used in the present study. The experimental findings demonstrated that proposed approach increased the classification performance of the non-optimized CNN architectures significantly and displayed a very high classification performance even in very low value of epoch.
Keywords: COVID-19; Colon cancer diagnosis; Convolutional neural network (CNN); Gradient-based optimizer (GBO); Hyperparameter optimization.
【저자키워드】 COVID-19, convolutional neural network (CNN), Colon cancer diagnosis, Gradient-based optimizer (GBO), Hyperparameter optimization., 【초록키워드】 coronavirus disease, coronavirus, pandemic, Cancer, RT-PCR, coronavirus disease-2019, Viral pneumonia, X-ray, CNN, Colorectal cancer, polymerase chain reaction, Region, healthcare, Algorithm, Convolutional neural network, Microarray, chest X-ray, Darknet, reverse transcription, receptor, PCR test, disease, epithelial, early stage, diagnose, Reverse transcription-polymerase chain reaction, Analysis, Colon cancer, epidermal growth factor, Chain Reaction, epoch, Colon, Chest X-ray image, growth factor, symptoms of COVID-19, National, datasets, parameter, Affect, approach, approach, significantly, the disease, were used, demonstrated, epidermal, diagnosis of COVID-19, 【제목키워드】 Diagnosis, CNN,