Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal International Journal of Artificial Intelligence Research
Muh Royan Fauzi Maulana
Institut Informatika dan Bisnis Darmajaya
Computer Science & IT Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Brain Tumor Detection On Magnetic Resonance Imaging Using Deep Neural Network Sutedi Sutedi; Muh Royan Fauzi Maulana; Melda Agarina; Arman Suryadi Karim; Nurfiana Nurfiana
International Journal of Artificial Intelligence Research Vol 7, No 1.1 (2023)
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i2.1061

Abstract

Cancer is a heterogeneous disease that can attack all parts of the body. Cancer is caused by the abnormal and uncontrolled growth of body cells, resulting in damage to body tissue and the potential to cause death. Cancer is a type of malignant tumor that attacks the body, one of which is the brain. Every year there are 300 brain tumor patients in Indonesia, both adults and children. Generally, doctors use two methods to diagnose these tumors, namely: biopsy and magnetic resonance imaging (MRI). Although the use of biopsy is quite accurate in identifying brain tumors, the time required is relatively long, reaching 15 days. While using MRI is relatively fast, the resulting accuracy is low and depends on the experience of medical personnel. This research aims to develop a method for diagnosing brain tumors using MRI images to make it faster and more accurate. The method used in this research was a deep neural network with a convolutional neural network (CNN) architecture layer. This method was chosen because deep learning has the advantage of pattern recognition with a high level of accuracy and is directly proportional to the number of datasets. This study used a dataset of 300 MRI images with two-dimensional (2D) axial imaging. The metrics used as a basis for the performance of the deep neural network model are accuracy, sensitivity, specificity, precision, and dice similarity coefficient with the results of each metric, namely: 99.3%, 98.6%, 98%, 98%, 98.3%. The research results showed that a deep neural network can speed up the diagnosis of brain tumors with high accuracy within 0.2 seconds.Â
Co-Authors - Nurfiana Arman Suryadi Karim, Arman Suryadi Melda Agarina Sutedi Sutedi