Simplified Convolutional Neural Network Application for Cervix Type Classification via Colposcopic Images.

The inner parts of the human body are usually inspected endoscopically using special equipment. For instance, each part of the female reproductive system can be examined endoscopically (laparoscopy, hysteroscopy, and colposcopy). The primary purpose of colposcopy is the early detection of malignant lesions of the cervix. Cervical cancer (CC) is one of the most common cancers in women worldwide, especially in middle- and low-income countries. Therefore, there is a growing demand for approaches that aim to detect precancerous lesions, ideally without quality loss. Despite its high efficiency, this method has some disadvantages, including subjectivity and pronounced dependence on the operator's experience. The objective of the current work is to propose an alternative to overcoming these limitations by utilizing the neural network approach. The classifier is trained to recognize and classify lesions. The classifier has a high recognition accuracy and a low computational complexity. The classification accuracies for the classes normal, LSIL, HSIL, and suspicious for invasion were 95.46%, 79.78%, 94.16%, and 97.09%, respectively. We argue that the proposed architecture is simpler than those discussed in other articles due to the use of the global averaging level of the pool. Therefore, the classifier can be implemented on low-power computing platforms at a reasonable cost.

Development of computer-aided model to differentiate COVID-19 from pulmonary edema in lung CT scan: EDECOVID-net.

The efforts made to prevent the spread of COVID-19 face specific challenges in diagnosing COVID-19 patients and differentiating them from patients with pulmonary edema. Although systemically administered pulmonary vasodilators and acetazolamide are of great benefit for treating pulmonary edema, they should not be used to treat COVID-19 as they carry the risk of several adverse consequences, including worsening the matching of ventilation and perfusion, impaired carbon dioxide transport, systemic hypotension, and increased work of breathing. This study proposes a machine learning-based method (EDECOVID-net) that automatically differentiates the COVID-19 symptoms from pulmonary edema in lung CT scans using radiomic features. To the best of our knowledge, EDECOVID-net is the first method to differentiate COVID-19 from pulmonary edema and a helpful tool for diagnosing COVID-19 at early stages. The EDECOVID-net has been proposed as a new machine learning-based method with some advantages, such as having simple structure and few mathematical calculations. In total, 13 717 imaging patches, including 5759 COVID-19 and 7958 edema images, were extracted using a CT incision by a specialist radiologist. The EDECOVID-net can distinguish the patients with COVID-19 from those with pulmonary edema with an accuracy of 0.98. In addition, the accuracy of the EDECOVID-net algorithm is compared with other machine learning methods, such as VGG-16 (Acc = 0.94), VGG-19 (Acc = 0.96), Xception (Acc = 0.95), ResNet101 (Acc = 0.97), and DenseNet20l (Acc = 0.97).

Immunological markers that correlate with protection immunity against tularemia infection.

An efficient immune response to tularemia is dependent on a strong cell-mediated component. We tried to identify markers of cellular immune responses that indicate a vaccine efficacy against tularemia. BALB/c mice were immunized with mutant F. tularensis 15∆23A and/or F. tularensis 15 NIIEG strains and then were challenged i.n. with F. tularensis Schu. We compared the influence of F. tularensis antigens (tularinum) in vitro on production of IL-1, IL-5, IL-6, IL-17, IFN-γ, and TNF-α by splenocytes obtained from intact mice and mice immunized with mutant F. tularensis 15∆23A and/or F. tularensis 15 NIIEG strains. We also compared expression of CD28, CD154, TLR-2, and CD69 markers on CD4 and CD8 T-cells after activation with tularinum in vitro. We found that tularinum-induced CD4(+) T-cells increased TNF-α and IFN-γ synthesis and expression of CD69 only in group mice with high degree of post immunization protection against F. tularensis Schu challenge. Estimation of CD69 expression on CD3(+)CD4(+) cells and IFN-γ, TNF-α synthesis by CD4(+) T-lymphocytes could be useful for determination protect ability of antitularemia immunity.