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All Journal EMITTER International Journal of Engineering Technology
Terawan Agus Putranto
RSPAD Gatot Subroto Presidential Hospital, Jakarta, Indonesia
Computer Science & IT

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Classification of Ischemic Stroke with Convolutional Neural Network (CNN) approach on b-1000 Diffusion-Weighted (DW) MRI Andi Kurniawan Nugroho; Dinar Mutiara Kusumo Nugraheni; Terawan Agus Putranto; I Ketut Eddy Purnama; Mauridhi Hery Purnomo
EMITTER International Journal of Engineering Technology Vol 10 No 1 (2022)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v10i1.694

Abstract

When the blood flow to the arteries in brain is blocked, its known as Ischemic stroke or blockage stroke. Ischemic stroke can occur due to the formation of blood clots in other parts of the body. Plaque buildup in arteries, on the other hand, can cause blockages because if it ruptures, it can form blood clots. The b-1000 Diffusion Weighted (DW) Magnetic Resonance Imaging (MRI) image was used in a general examination to obtain an image of the part of the brain that had a stroke. In this study, classifications used several variations of layer convolution to obtain high accuracy and high computational consumption using b-1000 Diffusion Weighted (DW) MR in ischemic stroke types: acute, sub-acute and chronic. Ischemic stroke was classified using five variants of the Convolutional Neural Network (CNN) architectural design, i.e., CNN1–CNN5. The test results show that the CNN5 architectural design provides the best ischemic stroke classification compared to other architectural designs tested, with an accuracy of 99.861%, precision 99.862%, recall 99.861, and F1-score 99.861%.
Co-Authors Andi Kurniawan Nugroho Dinar Mutiara Kusumo Nugraheni I Ketut Eddy Purnama Mauridhi Hery Purnomo