Determination of tumors by image processing applications in lung computerized tomography images

Türkiye’de bir yılda tespit edilen kanser vakalarının büyük çoğunluğu olan akciğer kanseri toplam vaka sayısının yaklaşık %20’sini oluşturmaktadır. En çok ölüm oranını oluşturan akciğer kanseri günümüzde Türkiye ve dünya için önemli bir sağlık sorunu durumundadır. Bu sorunun en önemli kaynağı erken tanısında tedavisi çok daha mümkün olan birçok vakanın erken teşhis edilememesidir. Bu çalışmada Bilgisayarlı Tomografi (BT) görüntüleri kullanılarak tümörlerin ve nodüllerin tespit edilmesi, görüntülerden çıkarılan özelliklerin farklı sınıflandırma algoritmaları ile sınıflandırılması amaçlanmıştır. Kullanılan görüntüler DICOM formatında olup RIDER-Lung CT veri setine ait 26 görüntü üzerinde çalışılmıştır. Tümör bölgesi farklı akciğer segmentasyon yöntemleri kullanılarak elde edilmiş, tümöre ait pek çok özellik hesaplanmıştır. Hesaplanan özelliklerden istatistiksel olarak anlamlı (p<0,05) olanları sınıflandırma için kullanılmıştır. Anlamlı özellikler Karar ağaçları (Decision Trees) algoritmaları, Destek Vektörü Makinesi (SVM), Yakın Komşuluk Sınıflandırması (KNN) sınıflandırıcı algoritmaları ve Diskriminant analizi ile sınıflandırılarak sonuçlar karşılaştırılmıştır. Bu algoritmaların doğruluk oranları karar ağaçları %97, SVM %96,6, KNN %93,6, Diskriminant analizi %97 olarak sonuç vermiştir. Yöntemler hassasiyet ve duyarlılık olarak karşılaştırıldığında ise her iki nicelik Kuadratik SVM ve Diskriminat analizinde % 95 üstüdür. Bu karşılaştırmalar sonucunda yöntemlerin yüksek başarı oranları ile umut verici olarak gelecek çalışmalarda kullanılabileceği görülmüştür.Lung cancer, which is the majority of cancer cases detected in a year in Turkey, constitutes approximately 20% of the total number of cases. Lung cancer, which constitutes the highest mortality rate, is an important health problem for Turkey and the world today. The most important source of this problem is that many cases that are much more possible to treat in early diagnosis cannot be diagnosed early. In this study, it is aimed to detect tumors and nodules using Computed Tomography (CT) images, and to classify features extracted from images with different classification algorithms. The images used are in DICOM format and 26 images of the RIDER-Lung CT data set were studied. The tumor region was obtained using different lung segmentation methods, and many features of the tumor were calculated. Statistically significant (p<0.05) calculated features were used for classification. Significant features were classified by Decision Trees algorithms, Support Vector Machine (SVM), Close Neighborhood Classification (KNN) classifier algorithms and Discriminant Analysis and the results were compared. The accuracy rates of these algorithms were 97% for decision trees, 96.6% for SVM, 93.6% for KNN, and 97% for Discriminant analysis. When the methods are compared in terms of sensitivity and sensitivity, both quantities are above 95% in Quadratic SVM and Discriminate analysis. As a result of these comparisons, it has been seen that the methods can be used in future studies with high success rates

Functional connectivity differences in brain networks from childhood to youth

Examining while using the resting-state functional magnetic resonance imaging (rs-fMRI) of brain development in terms of functional connectivity between childhood and adulthood in healthy subjects gives important information. In this study, it was aimed to examine the changes in functional networks between healthy age groups by using rs-fMRI for prepuberty (childhood), puberty, and adolescence by examining changes within and between networks. The rs-fMRI data in this study were obtained from the New York University Child Study in ADHD200 database. The data that 53 healthy subjects selected from the New York University dataset were split into three groups in the 8-9, 13-14, and 16-18 age ranges, and these groups included 18, 18, and 17 healthy subjects, respectively. The functional connectivity of data that preprocessed using the statistical parametric mapping routine was analyzed with ROI-to-ROI analysis by the CONN toolbox and the changes in the resting brain networks were obtained. Multiple voxel comparisons in statistical analyses were determined with the false discovery rate corrected p value (p-FDR), p < .05 for positive and negative correlations. According to our findings, within-network connectivity increases from childhood to young adulthood, and the connection between networks is weaker in childhood, also it has many dispersed connections, but less and strong links came out with advancing age. In addition, the brain begins to show a number of functional differences in adolescence, along with new changes that occur. The examination of resting-state networks in healthy children will make significant contributions to the scientific infrastructure and literature in terms of both identifying normal brain development and comparing it with neurological and psychiatric problems