Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Jurnal Ilmiah Kursor
Prasetyaning Estu Pratiwi
university of Pembangunan Nasional Veteran Jawa Timur
Computer Science & IT Decision Sciences, Operations Research & Management

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Optimization EfficientNetV2 model variant using Grad-CAM for multiple MRI brain tumor classification Denisa Septalian Alhamda; Wahyu Syaifullah J; Prasetyaning Estu Pratiwi; Surjo Hadi; Wan Suryani Wan Awang; I Gede Susrama Mas Diyasa
Jurnal Ilmiah Kursor Vol. 13 No. 3 (2026)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v13i3.428

Abstract

Fast and accurate diagnosis plays a critical role in effectively treating brain tumors. This study optimized and evaluated the EfficientNetV2 architecture through transfer learning, fine-tuning, and data augmentation, using three variants Small, Medium, and Large to classify MRI images into four categories: glioma, meningioma, pituitary tumors, and no tumor. Grad-CAM visualization was employed to enhance interpretability, providing a clear view of the critical regions in the MRI images that influenced the model’s decisions. Grad-CAM was tested across all model variants, and the best results were observed with EfficientNetV2-Large, where the model successfully highlighted the key areas associated with brain tumors. Among the variants, EfficientNetV2-Large achieved the best performance, with 99.85% accuracy, 99.60% precision, 99.65% recall, and 99.50% F1-score. However, this model required the longest computation time of 288 seconds per step, which may not be feasible in resource- constrained environments. Overall, this study underscores the potential of EfficientNetV2 models in revolutionizing brain tumor diagnosis by balancing accuracy, efficiency, and interpretability through advanced optimization techniques.Key words: Brain tumors, MRI classification, EfficientNetV2, Grad-CAM, Deep learning.
Co-Authors Denisa Septalian Alhamda I Gede Susrama Mas Diyasa Surjo Hadi Wahyu Syaifullah J Wan Suryani Wan Awang