Automatic Detection of Liver Cancer Using Hybrid Pre-Trained Models.

Liver cancer is a life-threatening illness and one of the fastest-growing cancer types in the world. Consequently, the early detection of liver cancer leads to lower mortality rates. This work aims to build a model that will help clinicians determine the type of tumor when it occurs within the liver region by analyzing images of tissue taken from a biopsy of this tumor. Working within this stage requires effort, time, and accumulated experience that must be possessed by a tissue expert to determine whether this tumor is malignant and needs treatment. Thus, a histology expert can make use of this model to obtain an initial diagnosis. This study aims to propose a deep learning model using convolutional neural networks (CNNs), which are able to transfer knowledge from pre-trained global models and decant this knowledge into a single model to help diagnose liver tumors from CT scans. Thus, we obtained a hybrid model capable of detecting CT images of a biopsy of a liver tumor. The best results that we obtained within this research reached an accuracy of 0.995, a precision value of 0.864, and a recall value of 0.979, which are higher than those obtained using other models. It is worth noting that this model was tested on a limited set of data and gave good detection results. This model can be used as an aid to support the decisions of specialists in this field and save their efforts. In addition, it saves the effort and time incurred by the treatment of this type of cancer by specialists, especially during periodic examination campaigns every year.

Study of Different Deep Learning Methods for Coronavirus (COVID-19) Pandemic: Taxonomy, Survey and Insights.

COVID-19 has evolved into one of the most severe and acute illnesses. The number of deaths continues to climb despite the development of vaccines and new strains of the virus have appeared. The early and precise recognition of COVID-19 are key in viably treating patients and containing the pandemic on the whole. Deep learning technology has been shown to be a significant tool in diagnosing COVID-19 and in assisting radiologists to detect anomalies and numerous diseases during this epidemic. This research seeks to provide an overview of novel deep learning-based applications for medical imaging modalities, computer tomography (CT) and chest X-rays (CXR), for the detection and classification COVID-19. First, we give an overview of the taxonomy of medical imaging and present a summary of types of deep learning (DL) methods. Then, utilizing deep learning techniques, we present an overview of systems created for COVID-19 detection and classification. We also give a rundown of the most well-known databases used to train these networks. Finally, we explore the challenges of using deep learning algorithms to detect COVID-19, as well as future research prospects in this field.

Successful canalization of a noncommunicating uterine horn by horn-vaginal anastomosis: preliminary findings of a novel approach for an unclassified anomaly.

PURPOSE: The aim of this study was to present the preliminary results of a new technique for horn-vaginal anastomosis. METHODS: Horn-vaginal anastomosis without any dissection at the lower pole of the horn or vaginal apex at the site of anastomosis. This was followed by dilation and silicone stent retention for 4 months. RESULTS: The patient was a 14-year-old presenting with primary amenorrhea and severe recurrent cyclic lower abdominal pain. The total operative time was 115 minutes. No operative complications were reported. The patient developed stenosis of the tract after 2 successive menstrual periods (MP). The third period was retained. Transvaginal dilatation of the communication tract was successfully accomplished, and a silicon stent was left in place for 5 successive MP. The patient is now menstruating in a regular pattern for 15 successive MP, and an office hysteroscopic examination showed a patent tract with a normal hemicavity leading to a normal tubal ostia. CONCLUSIONS: Communication between a well-developed noncommunicating uterine horn and vagina was accomplished with successful establishment of the menstrual outflow tract. Regular menstrual pattern was successfully reestablished for 15 consecutive menstrual periods.