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White blood cells (WBC), which form the basis of the immune system, protect the body from foreign invaders and infectious diseases. While the number and structural features of WBCs can provide important information about the health of people, the ratio of the subtypes of these cells and observable deformations are a good indicator in the diagnostic process. The recognition of cells of the type of lymphocytes, neutrophils, eosinophils, basophils and monocytes is critical. In this article, Deep Learning based Hybrid CNN (Convololutional Neural Network) model is proposed for classification of eosinophils, lymphocytes, monocytes, and neutrophils WBCs. The model presented is based on pretrained Alexnet and Googlenet architectures. The feature vector in the last pooling layer of both CNN architectures has been merged, and the resulting feature vector is classified by the Support Vector Machine. To determine the superiority of the proposed method, the classification was also performed and compared using pretrained Alexnet and Googlenet. Hybrid Alexnet-Googlenet-SVM model provides higher accuracy than pretrained Alexnet and Googlenet. The proposed method has been tested with WBC images from Kaggle and LISC database. Accuracy and F1-score were 99.73%, 0.99 and 98.23%, 0.98 for both data sets, respectively.

Our findings suggested these, MPV values in cases of appendicitis without complication were lower than the cases with complication and healthy control and MPV is a predictor of the development of complication in subjects with appendicitis.

Leukocytes, also known as white blood cells, are a group of cells that protect the body against infections, which is an important part of the immune system. The classification of white blood cells is widely used to diagnose various diseases, such as AIDS, leukemia, myeloma and anemia. However, the conventional methods to classify white blood cells are time consuming and prone to errors. In this paper, one of the most popular neural networks, convolutional neural network (CNN) is selected to differentiate between different types of white blood cells, namely, eosinophil, lymphocyte, monocyte and neutrophil. The CNN was coupled with Alexnet, Resnet50, Densenet201 and GoogleNet in turn, and trained with the Kaggle Dataset. Then, Gaussian and median filters were applied separately to the images in the database. The new images were classified again by the CNN with each of the four networks. The results obtained after applying the two filters to the images were better than the results obtained with the original data. The research results make it easier to diagnose blood related diseases.

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in Wuhan, China. This disease has spread to almost all countries in a short time. Countries take a series of stringent measures, including the prohibition of going out to prevent the virus that spreads COVID-19 disease. In this paper, we aimed to diagnose COVID-19 disease from X_RAY images by using deep learning architectures. In addition, 96.30% accuracy rate has been achieved with the hybrid architecture we have improved. While developing the hybrid model, the last 5 layers of Resnet 50 architecture were ejected. 10 layers were added in place of the 5 layers that were removed. The count of layers, which is 177 in the Resnet50 architecture, has been increased to 182 in the hybrid model. Thanks to these layer changes made in Resnet50, the accuracy rate has been increased more. Classification was performed with AlexNet, Resnet50, GoogLeNet, VGG16 and developed hybrid architectures using COVID-19 Chest X-Ray dataset and Chest X-Ray images (Pneumonia) datasets. As a result, when other scientific works in the literature are examined, it is finalized that the improved hybrid method offers better results than other deep learning architectures and can be used in computer-aided systems to diagnose COVID-19 disease.

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