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All Journal International Journal of Electrical and Computer Engineering Techno.Com: Jurnal Teknologi Informasi MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Jurnal Teknologi Informasi dan Ilmu Komputer JOIV : International Journal on Informatics Visualization Explore IT : Jurnal Keilmuan dan Aplikasi Teknik Informatika JURIKOM (Jurnal Riset Komputer) Jurnal Review Pendidikan dan Pengajaran (JRPP) JISKa (Jurnal Informatika Sunan Kalijaga) G-Tech : Jurnal Teknologi Terapan Journal of Computer System and Informatics (JoSYC) International Journal of Advances in Data and Information Systems Jurnal Nasional Teknik Elektro dan Teknologi Informasi Servis : Jurnal Pengabdian dan Layanan kepada Masyarakat
Irwan Budi Santoso
Universitas Islam Negeri Maulana Malik Ibrahim, Malang
Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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Journal : International Journal of Electrical and Computer Engineering

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Two-step majority voting of convolutional neural networks for brain tumor classification Santoso, Irwan Budi; Utama, Shoffin Nahwa; Supriyono, Supriyono
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp4087-4098

Abstract

Brain tumor type classification is essential for determining further examinations. Convolutional neural network (CNN) model with magnetic resonance imaging (MRI) image input can improve brain tumor classification performance. However, due to the highly variable shape, size, and location of brain tumors, increasing the performance of tumor classification requires consideration of the results of several different CNN models. Therefore, we proposed a two-step majority voting (MV) on the results of several CNN models for tumor classification. The CNN models included InceptionV3, Xception, DensNet201, EfficientNetB3, and ResNet50; each was customized at the classification layer. The initial step of the method is transfer-learning for each CNN model. The next step is to carry out two steps of MV, namely MV on the three CNN model classification results at different training epochs and MV on the results of the first step. The performance evaluation of the proposed method used the Nickparvar dataset, which included MRI images of glioma, pituitary, no tumor, and meningioma. The test results showed that the proposed method obtained an accuracy of 99.69% with a precision and sensitivity average of 99.67% and a specificity of 99.90%. With these results, the proposed method is better than several other methods.
Co-Authors Ahmad Fahmi Karami Ainul Yaqin Ambiya, Sri winduri Anwar, Aldian Faizzul Arafat, Ihsan Bagus Fahad Asep Sunarko Cahyani, Vivin Octavia Cahyo Crysdian Cahyo Crysdian Cahyo Crysdian Diantoro, Aris Dwi Purbo Yuwono Eko Agus Moh. Iqbal Galib, Galan Ramadan Harya Hamim Tohari Heni Prasetyo Ihda Noor Mujib Ma'muri, Ma'muri Mar Atul Ma’rifah Moh. Heri Susanto Mokhamad Amin Hariyadi Muhammad Ainul Yaqin Muhammad Faisal Nurussakinah, Nurussakinah Purwanto, Sofi Dwi Ririen Kusumawati Romadhani, Ahmad Shoffin Nahwa Utama Shoffin Nahwa Utama Sri Harini Sugiharto, Tomy Ivan Suhartono Supriyono Supriyono Supriyono Syahreni, Syahreni Totok Chamidy Zarkoni, Ahmad Zuhri, Abdurrozaq Ashshiddiqi