Classification of Brain Tumors on MRI Images Using DenseNet and Support Vector Machine

Agus Eko Minarno - Faculty of Engineering, Universitas Muhammadiyah Malang, Indonesia
Ilham Setiyo Kantomo - Faculty of Engineering, Universitas Muhammadiyah Malang, Indonesia
Fauzi Dwi Setiawan Sumadi - Faculty of Engineering, Universitas Muhammadiyah Malang, Indonesia
Hanung Adi Nugroho - Faculty of Engineering, Universitas Gadjah Mada, Yogyakarta, Indonesia
Zaidah Ibrahim - Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia


Citation Format:



DOI: http://dx.doi.org/10.30630/joiv.6.2.991

Abstract


The brain is a vital organ in the human body, performing various functions. The brain has always played a major role in the processing of sensory information, the production of muscular activity, and the performance of high-level cognitive functions. Among the most prevalent diseases of the brain is the development of aberrant tissue in brain cells, which results in the formation of brain tumors. According to data from the International Agency for Research on Cancer (IARC), more than 124,000 people worldwide were diagnosed with brain tumors in 2014, and more than 97,000 people died due to the condition. Current research indicates that magnetic resonance imaging (MRI) is the most effective means of detecting brain cancers. Because brain tumors are associated with significant mortality risk, a large number of brain tumor MRI imaging datasets were used in this research to detect brain cancers using deep learning techniques. To classify three forms of brain tumors, including glioma, meningioma, and pituitary, a deep learning model called DenseNet 201 paired with Support Vector Machines (SVM) was employed in this work included three types of brain tumors. Based on the results of the tests that were conducted, the best accuracy results obtained in this study were 99.65 percent, with a comparison ratio of 80 percent for training data and 20 percent for testing data, oversampled with the SMOTE method, with the best accuracy results obtained in this study being 99.65 percent.

Keywords


Brain; Tumor; MRI; SVM; CNN; Classification; Smote

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References


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