The analysis of white blood cells, which defend the body against deadly infections and disease-causing substances, is an important issue in the medical world. The concentrations of these cells in the blood, examined in 5 classes, i.e. monocytes, eosinophils, basophils, lymphocytes, and neutrophils, vary according to the types of diseases in the body. The peripheral blood smear is widely used to analyze blood cells. Manual evaluation of this method is laborious and time-consuming. At the same time, many environmental and humanistic parameters affect the method's performance. Therefore, in the presented study, a real-time detection process is realized. Firstly, YOLOv5s, YOLOv5x, and Detectron 2 R50-FPN pretrained models in the object recognition framework are used. Next, two original contributions are made to the study to improve the model's performance. The first contribution includes optimizing the activation function, an essential criterion in training the model, and an arrangement provided in the architecture. With this proposed approach, an improvement of 0.006 is achieved in the recognition rates of all classes. The second contribution is the combined use of the YOLO and Detectron2 frameworks, which have two different object evaluation processes. The success rate achieved with this hybrid structure provided an improvement between 3.44% and 14.7% compared to the outputs obtained from the YOLO and Detectron2 pretrained models. In addition, the maximum accuracy rate of this hybrid structure on the test dataset for detection and classification of white blood cells is obtained as 98%.

Organizmayı inşa etmek ve canlılığını sürdürmek için devasa bilgi barındıran DNA, önemli bir biyobelirteçtir. A,T,G ve C harflerinden oluşan sembolik bir dizilime sahip olan DNA genom parçası, protein üreten(ekson) ve protein üretmeyen(intron) kısımlardan meydana gelmektedir. Bu bölgelerin tanımlanması; kanserin gelişme durumunun incelenmesi, ilgili gen bölgelerinde mutasyonun gerçekleşip gerçekleşmediğinin izlenmesi ya da organizmanın büyüme ve gelişme durumlarının düzenlenmesi gibi farklı konuların aydınlatılmasında önemli bir role sahiptir. Bu kapsamda bilgisayar destekli sistemler ile ekson ve intron bölgelerinin doğru bir şekilde ayırt edilmesi hedeflenmiştir. Çalışmanın ilk aşamasında, farklı sayısal haritalama teknikleri ile sayısallaştırılan sembolik DNA dizilimleri üzerinde en başarılı sayısal haritalama tekniğine performans ölçütleri vasıtasıyla karar verilmiştir. Ardından ilk kısımda seçilen haritalama tekniği kullanılarak sayısallaştırılan DNA dizilimlerinin spektogram olarak ifade edilmesi sağlanmıştır. Zamanla değişen bir sinyalin frekans spektrumunun görsel bir temsili olan spektogramlar exon ve intron bölgeleri olarak etiketlendikten sonra öğrenme aktarımı olan EfficientNetB7 mimarisi ile sınıflandırılmıştır. Sınıflandırma sürecinin sonunda başarı oranı ve AUC değeri %100 olarak elde edilmiştir.

The eye is one of our five sense organs, where optical and neural structures are integrated. It works in synchrony with the brain, enabling the formation of meaningful images. However, lack of function, complete absence or structural abnormalities of cone cells in the cone cells in the retina causes the emergence of types of Color Vision Deficiency (CVD). This deficiency is characterized by the lack of clear vision in the use of colors in the same region of the spectrum, and greatly affects the quality of life of the patient. Therefore, it is important to develop filters that enable colors to be combined successfully. In this study, an original filter design was improved, built on a five-stage systematic structure that complements and supports itself. But optimization regarding performance value needs to be tested with objective methods independent of human decision. Therefore, in order to provide performance analyses based on objective evaluation criteria, original and enhanced images simulated by patients with seven different Color Vision Deficiency (CVD) types were classified with the MobileNet transfer learning model. The classification results show that the developed final filter greatly improves the differences in color perception levels in both eyes. Thus, color stimulation between the two eyes is more balanced, and perceptual symmetry is created. With perceptual symmetry, environmental colors are perceived more consistently and distinguishably, and the visual difficulties encountered by color blind individuals in daily life are reduced.

DNA is the part of the genome that contains enormous amounts of information related to life. Amino acids are formed by coding three nucleotides in this genome part, and the encoded amino acids are called codes in DNA. The frequency of the triple nucleotide in the DNA sequence allows for the evaluation of protein-coding (exon) and non-protein-coding (intron) regions. Distinguishing these regions enables the analysis of vital functions related to life. This study provides the classification of exon and intron regions for BCR-ABL and MEFV genes obtained from NCBI and Ensemble datasets, respectively. Then, existing DNA sequences are clustered using pretrained models in the scope of the SBERT approach. In the clustering process, K-Means and Agglomerative Clustering approaches are used consecutively. The frequency of repetition of codes is calculated with a representative sample selected from each cluster. The matrix is created using the frequencies of 64 different codons that constitute genetic code. This matrix is given as input to the ANFIS structure. The %88.88 accuracy rate is obtained with the ANFIS approach to classify exon and intron DNA sequences. As a result of this study, a successful result was produced independently of DNA length.

Electrocardiography (ECG) is a non-invasive tool used to identify abnormalities in heart rhythm. It is used to evaluate dysfunctions in the electrical system of the heart. It offers a mechanism that does not cause any harm to patients. Being affordable makes it accessible. It provides a comprehensive assessment of the condition of the heart. Although it provides a successful analysis opportunity for arrhythmia detection, it is time-consuming and depends on the clinician’s experience. In addition, since the ECG patterns in pediatric patients are different from the ECG patterns in adults, physicians consider it a difficult and complex task. For this reason, a custom dataset of pediatric patients was created in this study. This dataset consists of 1318 abnormal beats and 1403 normal beats. MobileNetv2 transfer learning architecture was used to classify this balanced dataset. However, the stability of the results is a valuable. Therefore, the optimization algorithm that minimizes the loss function and the regularization method that controls the complexity of the model are proposed. In this direction, Proposed Optimization Algorithm V5 and Proposed Regularization Method V5 approaches have been integrated into the MobileNetv2 transfer learning model. The accuracy rates produced in the training and test datasets are 0.9801 and 0.9509, respectively. These results have acceptable improvement and stability compared to the accuracies of 0.9633 and 0.9399 produced by the original MobileNetv2 architecture on the training and test dataset, respectively. However, performance values provide limited information about the generalizability of the model. Therefore, the same processes were repeated on a more complex dataset with 6 categories. As a result of the classification, the accuracy rates for the training and test data sets were obtained as 0.9200% and 0.8975%, respectively. Training was performed under the same conditions as the training performed on 2-category datasets. Therefore, it is normal for the test dataset to experience a decrease of approximately 5%. The results obtained show that generalizations can be made for comprehensive, highly diverse and rich datasets.