Segmentasi Dan Klasifikasi Wilayah Lesi Stroke Otak Menggunakan Neural Network

Muhammad, Fajar Azka Fadillah (2024) Segmentasi Dan Klasifikasi Wilayah Lesi Stroke Otak Menggunakan Neural Network. Masters thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Citra medis adalah citra yang lebih spesifik untuk kegiatan medis, terutama di otak. Masalahnya adalah bagaimana menemukan bagian irisan otak yang terdapat stroke. Kebanyakan studi sebelumnya mengarah ke Convolutional Neural Network (CNN) lebih spesifiknya ke U-net. Data pertama harus dipilah dan dibersihkan untuk mencapai neural network yang baik. U-net yang digunakan diharapkan bisa mengetahui lokasi irisan dengan melihat distribusi pembagian otak serta bisa mengetahui segmentasi stroke pada otak. U-net akan dilihat apakah bisa memecahkan data yang tidak ada pada pembelajaran mesin, hal ini ditujukan untuk mengetahui tingkat keberhasilan pembelajaran mesin.

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Medical images are images that are more specific to medical activities, especially in the brain. The problem is how to find the part of the brain slice where there is a stroke. Most previous studies lead to Convolutional Neural Network
(CNN) more specifically to U-net. The data must first be sorted and cleaned to achieve a good neural network. The U-net used is expected to be able to determine the location of slices by looking at the distribution of brain division and can determine the stroke segmentation in the brain. The U-net will be seen whether it can solve data that does not exist in machine learning, this is intended to determine the success rate of machine learning.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	U-net, CNN, Stroke Otak, Segmentasi, Wilayah Otak, U-net, CNN, Brain Stroke,Segmentation, Brain Slice
Subjects:	R Medicine > RZ Other systems of medicine T Technology > T Technology (General) > T57.5 Data Processing T Technology > T Technology (General) > T59.7 Human-machine systems.
Divisions:	Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Artificial Intelligence Engineering
Depositing User:	Muhammad Fajar Azka Fadillah
Date Deposited:	03 Aug 2024 09:12
Last Modified:	03 Aug 2024 09:13
URI:	http://repository.its.ac.id/id/eprint/113178

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