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Emine Avşar Aydin,Emine Avşar Aydin 대한전기학회 2019 Journal of Electrical Engineering & Technology Vol.14 No.5
The most common type of cancer for a female is breast cancer in the world. Regular checks and effective-timely treatment are noteworthy parameters for patients’ survival struggle . Against existing imaging methods, microwave imaging method has been considered more powerful and effective method by many researchers. In this paper, comprehensive design equations and parameters of rectangular microstrip patch antenna (RMPA) are given for microwave breast cancer detection. The layered breast model with a spherical tumor that was placed into the fibro-glandular layer was created by using CST Microwave Studio Software, and it was embedded in canola oil to decrease the distorted signals between the transmitting and receiving antennas. The RMPA has a wideband performance from 3 to 18 GHz. The simulation results show that differences in the electric field and reflection coefficients might more efficiently give a possibility to assign the tumor in the breast model. In addition, in this study, the data obtained from these experiments are classified by using the random forest algorithm from the data mining methods. According to the classification result, the random forest algorithm can diagnose breast cancer by classifying the tumor as 94% accuracy.
Cooking Quality and Sensorial Properties of Noodle Supplemented with Oat Flour
Emine Aydin,Duygu Gocmen 한국식품과학회 2011 Food Science and Biotechnology Vol.20 No.2
Effects of oat flour addition (10, 20, 30, and 40%) on the quality characteristics of noodle were investigated. Noodles were evaluated in terms of cooking quality, color, chemical, and sensory properties. As oat flour level increased, protein, crude fat, ash, Mn, Fe, Zn,and Mg contents of noodles increased. Oat flour caused increases in cooking loss of noodles. Sensory and cooking characteristics of noodles were negatively effected when oat flour level was increased compared with the control. Noodle with 10% oat flour received the highest sensory scores in all noodle samples containing oat flour. Overall acceptability scores of control and in only the noodle with 10% oat flour were found statistically (p<0.05) similar. Especially, the usage of 10% oat flour in noodle formulation gave satisfactory results in terms of acceptability.
A deep learning approach to permanent tooth germ detection on pediatric panoramic radiographs
Kaya, Emine,Gunec, Huseyin Gurkan,Aydin, Kader Cesur,Urkmez, Elif Seyda,Duranay, Recep,Ates, Hasan Fehmi Korean Academy of Oral and Maxillofacial Radiology 2022 Imaging Science in Dentistry Vol.52 No.-
Purpose: The aim of this study was to assess the performance of a deep learning system for permanent tooth germ detection on pediatric panoramic radiographs. Materials and Methods: In total, 4518 anonymized panoramic radiographs of children between 5 and 13 years of age were collected. YOLOv4, a convolutional neural network (CNN)-based object detection model, was used to automatically detect permanent tooth germs. Panoramic images of children processed in LabelImg were trained and tested in the YOLOv4 algorithm. True-positive, false-positive, and false-negative rates were calculated. A confusion matrix was used to evaluate the performance of the model. Results: The YOLOv4 model, which detected permanent tooth germs on pediatric panoramic radiographs, provided an average precision value of 94.16% and an F1 value of 0.90, indicating a high level of significance. The average YOLOv4 inference time was 90 ms. Conclusion: The detection of permanent tooth germs on pediatric panoramic X-rays using a deep learning-based approach may facilitate the early diagnosis of tooth deficiency or supernumerary teeth and help dental practitioners find more accurate treatment options while saving time and effort
A deep learning approach to permanent tooth germ detection on pediatric panoramic radiographs
Kaya Emine,Gunec Huseyin Gurkan,Aydin Kader Cesur,Urkmez Elif Seyda,Duranay Recep,Ates Hasan Fehmi 대한영상치의학회 2022 Imaging Science in Dentistry Vol.52 No.3
Purpose: The aim of this study was to assess the performance of a deep learning system for permanent tooth germ detection on pediatric panoramic radiographs. Materials and Methods: In total, 4518 anonymized panoramic radiographs of children between 5 and 13 years of age were collected. YOLOv4, a convolutional neural network (CNN)-based object detection model, was used to automatically detect permanent tooth germs. Panoramic images of children processed in LabelImg were trained and tested in the YOLOv4 algorithm. True-positive, false-positive, and false-negative rates were calculated. A confusion matrix was used to evaluate the performance of the model. Results: The YOLOv4 model, which detected permanent tooth germs on pediatric panoramic radiographs, provided an average precision value of 94.16% and an F1 value of 0.90, indicating a high level of significance. The average YOLOv4 inference time was 90 ms. Conclusion: The detection of permanent tooth germs on pediatric panoramic X-rays using a deep learning-based approach may facilitate the early diagnosis of tooth deficiency or supernumerary teeth and help dental practitioners find more accurate treatment options while saving time and effort.
Yasemin Sahan,Emine Aydin,Ayse Inkaya Dundar,Dilek Dulger Altiner,Guler Celik,Duygu Gocmen 한국식품과학회 2019 Food Science and Biotechnology Vol.28 No.5
In presented study total phenolic contents, antioxidant capacities and their bioaccessibilities from cookies supplemented with oleaster flour were investigated. Oleaster flours (OFs) were produced using two different methods (peeled oleaster flour: POF and unpeeled oleaster flour: UPOF) from two different genotypes. OFs were used to replace wheat flour in the cookie formulation (control) at the levels of 5, 10, 15, 20 and 25% (w/w). According to the results, enrichment of OFs clearly increased total phenolic contents, antioxidant capacities and bioaccessibilities of cookies. The highest bioaccessible antioxidant capacities (ABTS, CUPRAC, and FRAP) of the samples were obtained from cookie samples enriched with 25% UPOF-1. In conclusion, the increases in phenolic contents, antioxidant capacities, and bioaccessibilities from cookies supplemented with OFs suggest the potential enhancement of beneficial health effect of cookie due to increased content of bioactive compounds present in oleaster flour.