Brain Tumors Detection By Using Convolutional Neural Networks and Selection of Thresholds By Histogram Selection

Kasiful Aprianto, Kasiful Aprianto (2021) Brain Tumors Detection By Using Convolutional Neural Networks and Selection of Thresholds By Histogram Selection. Jurnal Ilmu Komputer dan Informasi, 14 (2). pp. 83-89. ISSN e-ISSN 2502-9274

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Abstract

Brain tumors in medical images have a high diversity in terms of shape and size. Some of the data found
a form between the tumor tissue and normal tissue, whereas knowing the tumor’s profile and characteristics
becomes a crucial part of searching. By using machine learning capabilities, where machines are given several
variables and provide decisions to a certain degree, they have broadly given decisions that support subject
matter in making decisions. This study applies the threshold selection method using histogram selection
on CT scan data, while the appropriate threshold selection method selects the tumor position accordingly.
Furthermore, the Convolutional Neural Network (CNN) is used to classify whether the selected image is a
tumor or not. Using CT scan data and calculated experiments, this algorithm can finally be approved and
given a brain classification with an accuracy of 75.42 percent.

Item Type:	Article
Uncontrolled Keywords:	Convolutional Neural Network, Histogram Selection, Machine Learning
Subjects:	T Technology > T Technology (General)
Divisions:	Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User:	Mrs Ni Made Yunia Dwi Savitri
Date Deposited:	18 Nov 2022 07:26
Last Modified:	18 Nov 2022 07:26
URI:	http://eprints.triatmamulya.ac.id/id/eprint/1849

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