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Objective: To determine the relationship between fatigue and sleep quality in medical students. Materials and Methods:We applied a sociodemographic characteristics questionnaire, Pittsburgh Sleep Quality Index (PSQI) and Piper Fatigue Scale (PFS) to 4 th , 5 th and 6 th year medical students.Results: Thirty seven percent (n=290) of 4 th , 5 th and 6 th year medical students were included in the study. Mean age was 23.47±1.33 years, 53.8% were male. Alcohol use was determined at a level of 40.3% (n=117), nutrition-drug usage at 40.7% (n=118), and smoking at 19% (n=55). Additionally, 86.2% of the participants (n=250) preferred to sleep in the dark, 37.9% (n=110) thought that drinks they used before going to bed partially affected their sleep quality. PSQI score was 10.56±2.54 (min:5-max:19), 98.6% of the participants had poor sleep quality. There was no significant relationship between the students' academic years and sleep quality. PFS was 2.85±0.83(min:1-max:5), and 79%(n=229) had mild fatigue. A statistically significant relationship was determined between PSQI and PFS scores (p<0.05). Conclusion:Although a large proportion of participants had bad sleep quality, level of fatigue was mild. We determined a significant relationship between levels of fatigue and sleep quality. Arrangements should be made to improve poor sleep quality which affects students' quality of life. Fatigue levels that affect sleep quality should also be reduced. Bulgular: Dönem 4,5 ve 6. sınıf tıp öğrencilerinin %37'si (n=290) çalışmaya dahil edilmiştir. Katılımcıların yaş ortalaması 23,47±1,33 olup %53,8'i erkekti. Sigara kullanımı %19 (n=55), alkol kullanımı %40,3 (n=117), uyanık kalmak için ilaç-besin takviye kullanımı %40,7 (n=118) bulundu. Katılımcıların %86,2'si(n=250) karanlıkta uyumayı tercih ederken %37,9'u (n=110) yatmadan önce tüketilen içeceklerin uyku kalitesini kısmen etkilediğini düşünüyordu. Çalışmamızda toplam PUKİ ortalaması 10,56±2,54 (min:5-maks:19) bulundu. Tüm katılımcılar içerisinde uyku kalitesi kötü olanlar %98,6 idi. Öğrencilerin bulunduğu sınıflar arasında uyku kalitesi yönünden istatistiksel olarak anlamlı fark yoktur (p>0,05). Araştırmamızda PYÖ puan ortalaması 2,85±0,83(min:1-maks:5) olup katılımcıların %79'unda (n=229) hafif düzeyde yorgunluk bulunmuştur. Katılımcıların yorgunluk ölçeği puanı ortalamalarıyla, PUKİ puanı arasında istatistiksel olarak anlamlı fark bulunmuştur (p<0,05). KeywordsSonuç: Katılımcıların büyük bir oranı kötü uyku kalitesine sahip olmakla birlikte yorgunluk seviyeleri hafif düzeyde bulunmuştur. Çalışmamızda yorgunluk düzeyi ve uyku kalitesi arasındaki ilişki anlamlıdır. Öğrencilerin yaşam kalitesini etkileyen kötü uyku kalitesini düzeltmeye yönelik düzenlemeler yapılmalı, uyku kalitesine yüksek oranda katkısı bulunan yorgunluk düzeyleri azaltılmaya çalışılmalıdır.

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The identification of leucocyte, also named white blood cells, types in histological blood tissue images is significant because it enables an opportunity for the diagnosis of various hematological diseases. In this study, for the diagnosis of lymphoma cancer, a hematologic disorder, we presented automatic detection and classification model using a deep learning approach. Faster R-CNN, which is a kind of region-based Convolutional Neural Network (CNN) model, achieves satisfactory performance on object detection and classification problems. To dispose of the feature extraction process in image-based applications, we offer a ResNet50 modified Faster R-CNN model for the detection and classification of leucocyte types which are lymphocyte, monocyte, basophil, eosinophil, and neutrophil in histological blood tissue images. In parallel with this purpose, a novel Faster R-CNN object detection model was designed by modifying ResNet50 model and the locations of leucocytes in the image were determined and classified. The efficiency of the proposed model was tested on a novel histological dataset including blood tissue images. The number of lymphocytes in the blood tissue is used as an evaluation criterion in the diagnosis of lymphoma cancer. Therefore, this study sets an example for clinical studies. According to the proposed model, firstly, the blood tissue images are normalized, and the implicit features are extracted by using the trainable convolution kernel. Then, for the reduction of the extracted implicit features, the maximum pooling is applied. After that, Region Proposal Networks (RPNs) are used to generate high-quality region proposals, which are used by Faster R-CNN for detection. Finally, the softmax classifier and regression layer are carried out to categorize the leucocyte types and estimate the boundary boxes of the test samples, respectively. Experimental results show the successful performance and the generalization capability of novel Faster R-CNN for the detection and classification of leucocyte types. This model demonstrates the potential to be deployed as a diagnostic tool for clinical studies because the method has been tested on a real-world histological data set.