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

Kasiful Aprianto

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.


Keywords


Convolutional Neural Network, Histogram Selection, Machine Learning

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DOI: https://doi.org/10.21609/jiki.v14i2.859

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