The COVID-19 diagnostic approach is mainly divided into two broad categories, a laboratory-based and chest radiography approach. The last few months have witnessed a rapid increase in the number of studies use artificial intelligence (AI) techniques to diagnose COVID-19 with chest computed tomography (CT). In this study, we review the diagnosis of COVID-19 by using chest CT toward AI. We searched ArXiv, MedRxiv, and Google Scholar using the terms “deep learning”, “neural networks”, “COVID-19”, and “chest CT”. At the time of writing (August 24, 2020), there have been nearly 100 studies and 30 studies among them were selected for this review. We categorized the studies based on the classification tasks: COVID-19/normal, COVID-19/non-COVID-19, COVID-19/non-COVID-19 pneumonia, and severity. The sensitivity, specificity, precision, accuracy, area under the curve, and F1 score results were reported as high as 100%, 100%, 99.62, 99.87%, 100%, and 99.5%, respectively. However, the presented results should be carefully compared due to the different degrees of difficulty of different classification tasks.

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Abstract-The aim of this study is to apply the principle of multi-criteria decision making theories on various types of cancer treatment techniques. Cancer is an abnormal cell that divides in an uncontrolled manner, it is a growth (tumor) that starts when alterations in genes make one cell to grow and multiply rapidly. Eventually, these cells may metastasize to other tissues. The primary factors that influence the comprehensive treatment plan of cancer include, but not limited to genetic factors, patient general health condition, explicit characteristic of cancer, and even purpose of the treatment. Other factors which are also essential include treatment duration, cost of treatment, comfortability, side effects and percentage of survival rate. The latter factors play an important role in the course of treatment and are therefore needed in order to evaluate the several treatment procedures. The outcome of the decision-making theories on these treatment procedures will help the concerned parties such as the patients, oncologists, and the hospital management. The most common cancer treatment techniques were evaluated and compared based on certain criteria using Fuzzy PROMETHEE decision-making theory.

X-rays are ionizing radiation of very high energy, which are used in the medical imaging field to produce images of diagnostic importance. X-ray-based imaging devices are machines that send ionizing radiation to the patient's body, and obtain an image which can be used to effectively diagnose the patient. These devices serve the same purpose, only that some are the advanced form of the others and are used for specialized radiological exams. These devices have image quality parameters which need to be assessed in order to portray the efficiency, potentiality and negativity of each. The parameters include sensitivity and specificity, radiation dose delivered to the patient, cost of treatment and machine. The parameters are important in that they affect the patient, the hospital management and the radiation worker. Therefore, this paper incorporates these parameters into fuzzy PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation) multi-criteria decision theory in order to help the decision makers to improve the efficiency of their decision processes, so that they will arrive at the best solution in due course.

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