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All Journal JSM (Jurnal SIFO Mikroskil) Jurnal Teknologi Informasi dan Ilmu Komputer InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan JURNAL MEDIA INFORMATIKA BUDIDARMA Indonesian Journal of Artificial Intelligence and Data Mining METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Data Science: Journal of Computing and Applied Informatics Jambura Journal of Mathematics Building of Informatics, Technology and Science Jurnal Mantik INFOKUM Journal La Multiapp Engineering Science Letter Journal of Novel Engineering Science and Technology
Ronsen Purba
Magister Komputer, STMIK Mikroskil
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering

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Journal : Journal of Novel Engineering Science and Technology

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Optimizing Brain Tumor Classification with Freeze-5 VGG16 and Dataset Fusion Vicky, Vicky; Ronsen Purba
Journal of Novel Engineering Science and Technology Vol. 4 No. 02 (2025): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v4i02.999

Abstract

Magnetic resonance imaging (MRI)-based brain tumor classification is pivotal for early diagnosis and treatment planning. This study enhances the VGG16 pretrained model through freeze-5 fine-tuning (i.e., freezing the first five convolutional layers) and dataset fusion of two public repositories, yielding 5,023 training and 1,311 testing images. Preprocessing includes normalization and grayscale-to-RGB conversion, followed by moderate augmentation (rotation ≤ 15°, shift ≤ 0.1, zoom ≤ 0.1, brightness [0.9–1.1]). The base VGG16 (without top layers) is extended with GlobalAveragePooling2D, Dense (1024, ReLU), Dropout (0.5), and Dense (4, softmax) layers. The model is compiled with the Adam optimizer (lr=1e-4), EarlyStopping, and ReduceLROnPlateau callbacks. On the test set, the proposed configuration achieves peak accuracy of 99.16 % and macro-F1 of 0.99, outperforming prior hybrid approaches. An ablation study confirms that the freeze-5 strategy combined with data augmentation significantly boosts generalization without overfitting. These results underscore the critical role of optimal layer-freezing and dataset fusion in brain tumor classification. Future work will explore ensemble architecture and real-time clinical deployment.
Co-Authors Adek Efnaldi Rambe Aldo Alex Sitorus Ali Akbar Lubis Andri Andri, Andri Anisah - Anna Agustiana Anwar Utama Apin Ridwan Aritonang, Mendarissan Arwin Arwin Arwin Halim Arwin Halim Arwin Halim Asif Rana Br Sibarani, Sabrina Adela Erlina Halim Erwin Setiawan Panjaitan Fandi Presly Simamora Feredy Lestari Pandia Frans Agus Purba Harry Darmawan Hendra - Indra Syahputra Irpan Pardosi Jimmy Nganta Ginting Jimmy Peranginangin Kardika Sidabariba Kelvin Chen Lestari, Wulan Sri Manurung, Juliana Damayanti Mark Dhruba Sikder Maya Silvi Lidya Megawaty Simamora Muhammad Fermi Pasha Munthe, Andreas Nadya Sikana Pasha, Muhammad Fermi Pipin, Sio Jurnalis Poerianto Poerianto Ricky Paian Limbong Rivaldi Lubis Rudy Rudy Sari Fatmawati Sastra Wandi Nduru Sibarani, Sabrina Adela Br Sinaga, Frans Mikael Situmorang, Gilbert Fernando Sunario Megawan Vicky, Vicky Winardi, Sunaryo Wita Clarisa Ginting Yudi Pratama Hasibuan Yundari, Yundari