Abstract

Brain cancer is one of the most malignant types of cancer. Several computer-Aided diagnostic (CAD) systems have been created to help clinicians analyze MRI (magnetic resonance imaging) images and find brain tumors. In general, CAD systems use conventional methods to classify images, in which the results are very dependent on the quality of object segmentation in the image. Meanwhile, tumor objects have different shapes, colors, and sizes, so the difficulty level of segmentation is quite high. On the other hand, currently, deep learning algorithms have been developed which are generally able to provide high accuracy results. Therefore, in this study, a tumor detection method on brain MRI images based on Convolutional Neural Network (CNN) is proposed. CNN method is one of the deep learning algorithms that combine the processes of segmentation, feature extraction, and classification into one. The system performs this task totally on its own through a training phase. However, the performance of the CNN method highly depends on the sufficient amount of training data, which usually cant be fulfilled in the case of medical data. Therefore, we proposed a CNN architecture developed from Inception V3in which the training process utilizes the transfer learning method to overcome the small amount of training set. The experimental result shows that the proposed model gives high performance on a small-size dataset with accuracy, precision, recall, and F1-score of 84%, 91%, 76%, and 83%, respectively. Moreover, InceptionV3 gives high efficiency compared to the other non-lightweight network architecture, such as ResNet50V2, InceptionResNetV2, and VGG16.

Original languageEnglish
Title of host publicationProceedings - 9th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2022
EditorsMochammad Facta, Mohammad Syafrullah, Munawar Agus Riyadi, Imam Much Ibnu Subroto, Irawan Irawan
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages367-373
Number of pages7
ISBN (Electronic)9786239213558
DOIs
Publication statusPublished - 2022
Event9th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2022 - Jakarta, Indonesia
Duration: 6 Oct 20227 Oct 2022

Publication series

NameInternational Conference on Electrical Engineering, Computer Science and Informatics (EECSI)
Volume2022-October
ISSN (Print)2407-439X

Conference

Conference9th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2022
Country/TerritoryIndonesia
CityJakarta
Period6/10/227/10/22

Keywords

  • Convolutional Neural Network (CNN)
  • Inception V3
  • Magnetic Resonance Imaging (MRI)
  • brain tumor
  • deep learning
  • image classification

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