Classification of Hippocampal Region using Extreme Learning Machine

نویسندگان

  • Muhammad Hafiz Md Zaini
  • Mohd Ibrahim Shapiai
  • Ahmad Rithauddin Mohamed
  • Norrima Mokhtar
  • Zuwairie Ibrahim
چکیده

Important brain parts like hippocampal usually being manually segmented by doctors. But with the introduction of hybrid between machine learning along with neuroimaging technique, it has proved to shows some promising results regarding on segmenting subcortical structures. However, it is known that Extreme Learning Machine (ELM) is to be superior machine learning technique. This study will investigate on the usage of ELM to segment hippocampal by using various hidden nodes configuration. This study also will address on the usage of full image and region of interest (ROI) using ELM. Bag of features is used as a feature extractor where it will segment the hippocampal of the MRI in order to get its visual words. ELM will used it to learn its feature. Results shows that with suitable hidden nodes, it could achieve up to 100% performance on both cases for full image and ROI in hippocampal segmentation.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

A Hybrid Machine Learning Method for Intrusion Detection

Data security is an important area of concern for every computer system owner. An intrusion detection system is a device or software application that monitors a network or systems for malicious activity or policy violations. Already various techniques of artificial intelligence have been used for intrusion detection. The main challenge in this area is the running speed of the available implemen...

متن کامل

Modeling Discharge Coefficient of Side Weir on Converging Channel Using Extreme Learning Machine

In this study, the discharge coefficient of side weirs located on converging channels was simulated for the first time using a new method of Extreme Learning Machine (ELM). To examine the accuracy of the numerical model, the Monte Carlo simulations were used and the experimental values validation was conducted by the k-fold cross validation method. Then, the input parameters were detected for s...

متن کامل

Simulation of Scour Pattern Around Cross-Vane Structures Using Outlier Robust Extreme Learning Machine

In this research, the scour hole depth at the downstream of cross-vane structures with different shapes (i.e., J, I, U, and W) was simulated utilizing a modern artificial intelligence method entitled "Outlier Robust Extreme Learning Machine (ORELM)". The observational data were divided into two groups: training (70%) and test (30%). Then, using the input parameters including the ratio of the st...

متن کامل

Outlier Detection Using Extreme Learning Machines Based on Quantum Fuzzy C-Means

One of the most important concerns of a data miner is always to have accurate and error-free data. Data that does not contain human errors and whose records are full and contain correct data. In this paper, a new learning model based on an extreme learning machine neural network is proposed for outlier detection. The function of neural networks depends on various parameters such as the structur...

متن کامل

Fault Detection of Anti-friction Bearing using Ensemble Machine Learning Methods

Anti-Friction Bearing (AFB) is a very important machine component and its unscheduled failure leads to cause of malfunction in wide range of rotating machinery which results in unexpected downtime and economic loss. In this paper, ensemble machine learning techniques are demonstrated for the detection of different AFB faults. Initially, statistical features were extracted from temporal vibratio...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2017