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Background: The pandemic caused by coronavirus in recent months is having a devastating global effect, which puts the world under the most ever unprecedented emergency. Currently, since there are not effective antiviral treat- ments for Covid-19 yet, it is crucial to early detect and monitor the progression of the disease, thus helping to reduce mortality. While a corresponding vaccine is being developed, and different measures are being used to combat the virus, medical imaging techniques have also been investigated to assist doctors in diag- nosing this disease. Objective: This paper presents a practical solution for the detection of Covid-19 from chest X-ray (CXR) images, exploiting cutting-edge Machine Learning techniques. Methods: We employ EfficientNet and MixNet, two recently developed families of deep neural networks, as the main classifica- tion engine. Furthermore, we also apply different transfer learning strategies, aiming at making the training process more accurate and efficient. The proposed approach has been validated by means of two real datasets, the former consists of 13,511 training images and 1,489 testing images, the latter has 14,324 and 3,581 images for training and testing, respectively. Results: The results are promising: by all the experimental configurations considered in the evaluation,our approach always yields an accuracy larger than 95.0%, with the maximum accuracy obtained being 96.64%. Conclusions: As a comparison with various existing studies, we can thus conclude that our performance improvement is significant.

One in four women is estimated to be diagnosed with breast cancer during her lifetime, making breast cancer the commonest cancer among women worldwide (Bray et al., 2018). In Vietnam, breast cancer accounts for 8.9% of new cancer cases among women each year (International Agency for Research on Cancer, 2012). In addition, most Vietnamese patients receive diagnosis at an extremely late stage (Thuan, Anh, Tu, & Huong, 2017). According to a study conducted at the five largest cancer hospitals in Vietnam, 49.5% of newly diagnosed breast cancer patients received diagnosis at stages III and IV (Thuan et al., 2017). Moreover, nearly half of Vietnamese patients with breast cancer resided in rural areas (Hoang Lan, Laohasiriwong, Stewart, Tung, & Coyte, 2013), where women have low levels of education and income and limited access to health care. Furthermore, a high proportion of breast cancer patients in Vietnam are relatively young, and one-third of patients are diagnosed at a younger age (Hoang Lan et al., 2013) than are patients in Western countries (Youlden, Cramb, Yip, & Baade, 2014) (40-49 versus 55-65 years). Evidence shows that early screening and access to optimal treatment can improve survival rates in patients with breast cancer