Classification of Ischemic Stroke with Convolutional Neural Network (CNN) approach on b-1000 Diffusion-Weighted (DW) MRI

Andi Kurniawan Nugroho (Institut Teknologi Sepuluh Nopember (ITS) Surabaya)
Dinar Mutiara Kusumo Nugraheni (Department of Computer Science, Universitas Diponegoro , Semarang, Indonesia)
Terawan Agus Putranto (RSPAD Gatot Subroto Presidential Hospital, Jakarta, Indonesia)
I Ketut Eddy Purnama (Department of Computer Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia)
Mauridhi Hery Purnomo (Electrical Engineering Department, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia)

EMITTER International Journal of Engineering Technology
Vol 10 No 1 (2022)

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%.