RESUMEN
In recent years, almost every country in the world has struggled against the spread of Coronavirus Disease 2019. If governments and public health systems do not take action against the spread of the disease, it will have a severe impact on human life. A noteworthy technique to stop this pandemic is diagnosing COVID-19 infected patients and isolating them instantly. The present study proposes a method for the diagnosis of COVID-19 from CT images. The method is a hybrid method based on convolutional neural network which is optimized by a newly introduced metaheuristic, called marine predator optimization algorithm. This optimization method is performed to improve the system accuracy. The method is then implemented on the chest CT scans with the COVID-19-related findings (MosMedData) dataset, and the results are compared with three other methods from the literature to indicate the method's performance. The final results indicate that the proposed method with 98.11% accuracy, 98.13% precision, 98.66% sensitivity, and 97.26% F1 score has the highest performance in all indicators than the compared methods which shows its higher accuracy and reliability.
Asunto(s)
Algoritmos , Prueba de COVID-19/métodos , COVID-19/diagnóstico por imagen , Redes Neurales de la Computación , Tomografía Computarizada por Rayos X/métodos , COVID-19/metabolismo , COVID-19/patología , COVID-19/virología , Exactitud de los Datos , Bases de Datos Factuales , Aprendizaje Profundo , Humanos , Procesamiento de Imagen Asistido por Computador/métodos , Modelos Teóricos , Reproducibilidad de los Resultados , Proyectos de Investigación , SARS-CoV-2/aislamiento & purificación , Sensibilidad y EspecificidadRESUMEN
OBJECTIVE: Several denoising methods for medical images have been applied, such as Wavelet Transform, CNN, linear and Non-linear methods. METHODS: In this paper, A median filter algorithm will be modified and the image denoising method to wavelet transform and Non-local means (NLM), deep convolutional neural network (Dn- CNN), Gaussian noise, and Salt and pepper noise used in the medical image is explained. RESULTS: PSNR values of the CNN method are higher and showed better results than different filters (Adaptive Wiener filter, Median filter, and Adaptive Median filter, Wiener filter). CONCLUSION: Denoising methods performance with indices SSIM, PSNR, and MSE have been tested, and the results of simulation image denoising are also presented in this article.