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Background and Aim Breast cancer is the abnormal cell growth in the breast. In both benign and malignant masses, there is rapid and high cell growth. Nowadays, due to the development of technologies, the diagnosis of diseases has become non-invasive and physicians attempts to diagnose the disease without surgery and based on internal organ images. Methods & Materials In this study, by using images prepared from the Digital Database for Screening Mammography (DDSM), a new method is proposed for detecting cancerous masses in the mammographic images using geometric features extraction and optimization of Support Vector Machine (SVM) parameters to classify breast cancer masses automatically. First, images were pre-processed and then boundaries were determined using threshold method. Next, morphological operators were used to improve these boundaries and the segmentation of images was carried out to classify cancerous masses. Finally, by using the SVM parameter optimization method, Grasshopper Optimization Algorithm (GOA), and 4-fold crossvalidation method, data were classified into two groups of benign and malignant (cancer) masses. Ethical Considerations Images from DDSM database were used in this research, all images are open access in this database. Results The accuracy, sensitivity and specificity values for applying the Radial Basis Function (RBF) kernel in SVM classifier (before optimization process) were obtained 97%, 100% and 96, respectively. After optimization of SVM parameters by the GOA, it was reported 100% for all accuracy, sensitivity and specificity indices for applying linear kernel function, indicating the high accuracy of the proposed method. The average values of accuracy, sensitivity and specificity indices for applying all three SVM kernel functions after optimization were 95.83, 100 and 94.81%, respectively. Conclusion The extracted geometrical features from breast cancer masses are highly efficient for model training and the diagnosis of breast cancer. The GOA could improve the overall accuracy of the proposed method by optimizing the SVM parameters. The results showed the higher performance of the proposed method compared to other methods. [ABSTRACT FROM AUTHOR]

Background and Objectives Change in the age structure of the population affects many activities and processes, and most countries are expected to enter the old population phase by 2050. Today, for various reasons, the consumption of blood and blood products has increased. The aim of this study was to investigate the age change in donor population during the years 1390 to 1397 and compare it with the society's age change algorithm to determine if the latter affects the former. Materials and Methods In this cross-sectional descriptive study, demographic information of 329631 blood donors from 1390 to 1397 from Blood Transfusion Software (Negareh software version 5/9) was extracted and the latest demographic information of the province was achieved using the website of the Statistics Center of Iran. Results Of the population of Yazd and Ardakan cities, 64.7% and 65.38% are within the appropriate age range for blood donation. The donor population of 18-30 years decreased by 30.7% in 2016 and 32.3% in 2018 compared with the year 2011 while other age groups almost increased. Mean age of donors in 2014 and 2017 were 44.2 and 40.05 years, respectively. Conclusions Due to the aging population, and the reduction of the population under the age of 30, it is necessary to take effective programs to maintain and attract 18-30 year old population for blood donation. [ABSTRACT FROM AUTHOR]

Background & Aims: Cancer is one of the great human challenges in all countries, both advanced and developing. Cancer treatment management can include surgery, chemotherapy, or radiation therapy (1). Radiation therapy is done in two ways: Teletherapy and Brachytherapy. Brachytherapy involves the use of radiation sources to treat cancer by irradiating cancerous tissue from within the patient’s body (2). But the dose and how to use this method has always been questionable for researchers. Therefore, this study creates a new fuzzy approach to high-dose brachytherapy by optimizing the distribution of double roughness based on dosimetric criteria. The use of fuzzy logic itself has increased the accuracy of the mathematical model of the problem. This fuzzy model is a new study and innovation used in this paper. Due to the fuzzy nature of this method and its limitations, it is considered fuzzy. This makes the method more accurate and includes parameters such as the patient’s physical ability and age in the problem, which in itself increases the accuracy of the method for each patient. As a result, the obtained answer is improved and the executive program of brachytherapy method is more accurate. Methods: In the present study, the dose prescribed for an organ was evaluated by dosimetric indices listed in Table 1 (18). For the present study, data from 20 patients in the age range of 50 to 74 and mean age 62 years with a wide range of prostate volume between 23 and 103 cubic centimeters, and for the treatment of prostate cancer by brachytherapy from the Academic Medical Center (AMC, Amsterdam, the Netherlands) had participated. To compare brachytherapy programs with high interstitial dose, the dose rate was calculated with 192Ir beam with a radiation dose of 13 Gy, according to the standard protocol TG-43. To begin with, computed tomography (CT) scans or magnetic resonance imaging (MRI) were taken from the patients pelvis, and entered into the treatment planning software for use in treatment planning sessions. BT treatment planners and specialists then determined the input catheters, target volumes, and OARs obtained from the medical images. Depending on the size and exact location of the target volumes, between 14 and 20 catheters entered the patient’s body, reaching the target volumes. After designing and approving an acceptable treatment plan, the catheters inserted into the patient’s body were connected to a retractor 4 that controls the movement of the radiation source. After the treatment program, the source was returned to the retractor (8, 14). Then, an integer program model with fuzzy constraints was proposed for programming on high-dose brachytherapy. The description of infrastructures, parameters and variables in this model are in Table 2 (15 ). Finally, the dosimeter index is equal to the sum of all the index variables, as it turned out: The proposed model is a correct programming model called IP. In general, three evolutionary algorithms (EP), (GA) and (ES) were used in this research. The algorithms stopped after creating 20 generations of desirable answers and the best answer of each generation was determined on the chart as a point. The resulting set of answers were connected in the form of graphs, which are used to analyze the results. Each point on the graph identifies the best answer from each of the generations generated by the respective algorithms. These three algorithms were performed independently for each patient and the obtained answers were identified as dots on the chart. The graph obtained for each patient indicates the capability of each of these algorithms. Results: According to this study, each of the three algorithms (EP), (GA) and (ES) are each run independently for each of the patients with prostate cancer. At first, the genetic algorithm showed the ability to get closer to the desired answer sooner, but as the optimization process continues, the rate of convergence to the desired answer decreases, so if the time parameter is very important, the genetic algorithm can be useful. Especially for patients whose prostate volume was larger than other patients. In patients 15, 12, 8 and 9, due to their younger age than other patients and better physical condition, as well as prostate volume less than 80 ml, they had much more promising results than other patients. In contrast, for patients 4 and 19 with an age of over 70, the results were not as favorable as for other patients. The * sign in the table indicates that there is no answer through that patient-specific algorithm that covers 95 or even more of the tumor volume. Therefore, according to Table 2, it can be concluded that the best algorithm that can be considered for the case where the shortest time to reach the target coverage above 95% is the genetic algorithm. According to Table 2, it can be concluded that the best algorithm that can be considered for the case where the shortest time to reach the target coverage above 95% is the genetic algorithm. Therefore, according to Table 3, the ES algorithm has better answers than the other two algorithms for the case in which the largest volume of the tumor is covered. According to the results, the ES algorithm has obtained the best results for patients under 60 years of age and normal prostate volume. Conclusion: According to the obtained results, it can be stated that whether the main goal is the maximum coverage or the goal is the shortest possible time to reach the coverage above 95%, the best algorithm that can get a good answer for each patient is the evolutionary strategy algorithm. [ABSTRACT FROM AUTHOR]

Background. Cervical cancer begins in superficial cells and over time can invade deeper tissues and surrounding tissues. This paper presents a creative idea of using an ensemble classification algorithm that improves the predictive performance of an artificial intelligence system based on cervical cancer screening. This study aimed to classify Pap-smear images by different machine learning methods to achieve high accuracy detection. Methods. This study was performed on 917 Pap-smear images from the Herlev public database. In the feature extraction stage, 20 geometric features and 76 texture features were extracted. After that, using ensemble classification method, the images were classified into two categories (i.e., normal and abnormal) and then into seven categories (i.e., superficial epithelial, intermediate epithelial, columnar epithelial, mild dysplasia, moderate dysplasia, severe dysplasia and carcinoma) and the accuracy of the proposed method was evaluated. Results. The algorithm in the ensemble classification was able to achieve accuracy of 99.9% with a processing time of 0.028 second in the two-class classification and accuracy of 76.5% with a processing time of 0.033 second in the seven-class classification. Conclusion. Based on the results, the designed algorithm can be used as a computer aided diagnostic tool to increase the accuracy and speed of predicting the risk of cervical cancer. Practical Implications. Cervical cancer is one of the most common cancers among women. Early diagnosis of the disease can save various costs and prevent the patients’ frequent visits to medical centers. This research proposed an artificial intelligence method for automatic classification of cervical cells and improving the accuracy of diagnosis. [ABSTRACT FROM AUTHOR]