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Article Citation - WoS: 5Citation - Scopus: 7Comparison of Nat1, Nat2 & Gstt2-2 Activities in Normal and Neoplastic Human Breast Tissues(Aepress Sro, 2006) Geylan-SU, YS; Isgör, B; Coban, T; Kapucuoglu, N; Aydintug, S; Iscan, M; Güray, T; Chemical EngineeringIn this study, arylamine N-acetyltransferases, NATs (E.C.2.3.1.5) and glutathione-S-transferase-T2-2, GSTT2-2 (E.C.2.5.1.18) enzyme activities in the breast tumor and surrounding tumor-free tissues of 22 female breast cancer patients with infiltrating ductal carcinoma were measured. The possible impacts of grade of malignancy, chemotherapy treatment, estrogen receptor status and menopausal status on all enzyme activities were evaluated. The results showed that, both NAT2 and GSTT2-2 display significant differences between tumor and tumor-free breast tissues, while no difference was observed in NAT1. Grade of malignancy seems to be positively associated with NAT1 and negatively associated with GSTT2-2. Though, both NAT2 and GSTT2-2 have increased mean tumor activities, the grade of malignancy, chemotherapy status, menopausal status or estrogen receptor status are not correlated statistically.Conference Object Citation - WoS: 3Citation - Scopus: 20Comparison of Breast Cancer and Skin Cancer Diagnoses Using Deep Learning Method(Ieee, 2021) Bilgic, BurcuArtificial intelligence applications are of great importance in the solution of cancer, which is one of the biggest health problems of our age. In this study, a study was conducted on deep learning methods that make life important in the early diagnosis of breast cancer and skin cancer, which are among the most common types of cancer worldwide. Breast cancer and skin cancer data were classified as benign and malignant by deep learning methods. While working with the deep learning method, the classification was made using the Convolutional Neural Network (CNN) algorithm. In this classification, the data are divided into benign cancer sets and malignant cancer sets. Finally, the data provided by the logistic regression method were analyzed and success charts were created and both types were compared. As a result, accuracy and loss graphs of both cancer types were formed. The aim of the study is to compare breast cancer and skin cancer with the deep learning method. And some breast cancer and skin cancer diagnoses are confused. In further studies, the basis of differentiating the diagnosis of these two types of cancer from each other was made in this study.
