Publications by authors named "Ziya EkSİ"
Some multiple sclerosis (MS) lesions may have great similarities with neoplastic brain lesions in magnetic resonance (MR) imaging and thus wrong diagnoses may occur. In this study, differentiation of MS and low-grade brain tumors was performed with computer-aided diagnosis (CAD) methods by magnetic resonance spectroscopy (MRS) data. MRS data belonging to 51 MS and 39 low-grade brain tumor patients were obtained.
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- MRI is crucial for diagnosing and monitoring multiple sclerosis (MS), but distinguishing between its forms, especially RRMS and SPMS, remains challenging.
- The study utilized MR spectroscopy and machine learning to automatically classify participants into healthy controls, RRMS, and SPMS, focusing on the metabolite N-acetylaspartate (NAA) for differentiation.
- Results showed high accuracy in classifying RRMS from healthy controls (85%) and between RRMS and SPMS (83.33%), suggesting that combining MRS with machine learning could enhance MS diagnosis.
Aim: The aim was to develop a high-performance computer-aided diagnosis (CAD) system with image processing and pattern recognition in diagnosing pancreatic cancer by using endosonography images.
Materials And Methods: On the images, regions of interest (ROI) of three groups of patients (<40, 40-60 and >60) were extracted by experts; features were obtained from images using three different techniques and were trained separately for each age group with an Artificial Neural Network (ANN) to diagnose cancer. The study was conducted on endosonography images of 202 patients with pancreatic cancer and 130 noncancer patients.