Recognition Covid-19 cases using deep type-2 fuzzy neural networks based on chest X-ray image

Today, the new coronavirus (Covid-19) has become a major global epidemic. Every day, a large proportion of the world's population is infected with the Covid-19 virus, and a significant proportion of those infected dies as a result of this virus. Because of the virus's infectious nature, prompt diagnosis, treatment, and quarantine are considered critical. In this paper, an automated method for detecting Covid-19 from chest X-ray images based on deep learning networks is presented. For the proposed deep learning network, a combination of convolutional neural networks with type-2 fuzzy activation function is used to deal with noise and uncertainty. In this study, Generative Adversarial Networks (GANs) were also used for data augmentation. Furthermore, the proposed network is resistant to Gaussian noise up to 10 dB. The final accuracy for the classification of the first scenario (healthy and Covid-19) and the second scenario (healthy, Pneumonia and Covid-19) is about 99% and 95%, respectively. In addition, the results of the proposed method in terms of accuracy, precision, sensitivity, and specificity in comparison with recent research are promising. For example, the proposed method for classifying the first scenario has 100% and 99% sensitivity and specificity, respectively. In the field of medical application, the proposed method can be used as a physician's assistant during patient treatment

Identification of Effective Genes of Multiple Cancers Using Neural Network

Cancer is a major health concern that affects a significant number of people worldwide and can often result in fatalities. Therefore, there is a growing need to develop effective approaches for early diagnosis and classification of different types of cancer. Early detection of cancer is crucial for prompt and accurate treatment. Thus, researchers have been working to identify non-invasive and precise methods for the early diagnosis, monitoring, and control of cancer. Leukemia and prostate cancer are two of the most common types of cancer globally. Microarray data analysis has become a valuable tool for diagnosing and classifying different types of cancerous tissues. To improve the accuracy of diagnosis, hybrid algorithms and neural networks are being employed. This paper provides a review of different biomarkers for leukemia and prostate cancer and proposes a novel method for distinguishing between the two cancers. The proposed method includes appropriate gene selection, a new hybrid model, and differential analysis of microarray data to create a diagnostic tool. The results indicate that the proposed algorithm is highly accurate and efficient in selecting a small set of valuable genes to improve classification accuracy. In conclusion, the accurate diagnosis and classification of cancer are essential for timely and effective treatment. The proposed method can contribute to the development of a reliable diagnostic tool for leukemia and prostate cancer, and the application of microarray data and hybrid algorithms can be useful for diagnosing other types of cancer as well