Alzheimer’s Disease (AD) is an irreversible disease that gradually worsens with time. Therefore, early diagnosis of Alzheimer’s disease is important to prevent brain tissue damage and treat the patient properly. Mild Cognitive Impairment (MCI) is a prodromal stage of AD, which has no harm to the patient’s ability to have functional activities in daily life except a minor cognitive deficiency. Since MCI can be detected at the earliest stage of AD, it is critical to detect patients with MCI to delay the progression of AD. It is possible to distinguish patients with AD, MCI, and Normal Control (NC) from one another by the size of brain volume, hippocampus and patient’s clinical information. The brain and hippocampus gradually shrink in size and shape as AD develops. In this study, we propose a deep learning-based technique to classify patients with AD, MCI and NC by brain Magnetic Resonance (MR) images. Deep learning has shown human-level performance in a lot of studies including medical image analysis with constrained amount of training data. We propose a deep learning-based ensemble model which consists of 3 Convolutional Neural Networks (CNN)  with Network In Network (NIN)  architecture. The kernel size is 3x3 convolution followed by 1x1 convolution to reduce the number of trainable parameters and extract features for classification better. In addition, Global Averaging Pooling (GAP) is used instead of Fully-Connected (FC) layers to avoid overfitting by reducing the number of trainable parameters. By using the ensemble model, this shows the 81.66% in classifying 3 classes.
Computed tomography (CT) has been used to obtain 3D data from an object or patient. However, most of CT uses polychromatic energy and that results in beam hardening artifact. Therefore, many methods for correcting beam hardening were proposed. Linearization method and post reconstruction method are main category of beam hardening correction method. Especially empirical approaches were commonly used at linearization method; however, empirical methods do not guarantee the linearity of projection data because it uses reconstructed image to decide linearity. Therefore, corrected images are not monochromatic CT images because we could not specify the energy. Proposed method use linearization method as a basic concept. However, we had considered about the relationship between path length and projection data and then found a way to specify the energy of corrected images because proposed method linearizes the projection data fundamentally. Moreover, calculation time for making corrected sinogram was very short. Therefore, this method can be used practically.
Computed tomography (CT) has been used for medical purposes. However there are many artifacts at CT images and
that makes distorted image. Ring artifact is caused by non-uniform sensitivity of detectors and makes ring shape artifact.
Line-ratio method was proposed to solve the problem however there are some problem at specific case. Therefore we
propose advanced method to correct ring artifact using transfer function. As a result, ring artifacts can be removed at
more global cases. Simulation data shows the proposed method outperforms the conventional line-ratio method.