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Mumtaazah, Muhammad Athar
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Computer Science & IT Engineering

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Comparative Analysis Using Xception and MobileNetV2 Deep Learning Models for Brain Tumor Detection in MRI Images Mumtaazah, Muhammad Athar; Anik Nur Handayani
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 1 (2025): Issues July 2025
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i1.15332

Abstract

This study presents a comparative analysis of two deep learning models, Xception and MobileNetV2, for brain tumor detection using MRI images. The selection of these models is based on their respective advantages. Xception is known for its ability to handle large and complex datasets due to its deep architecture and the use of depthwise separable convolutions. It also features a deep structure capable of extracting complex features from high-resolution images, making it well-suited for detailed image recognition tasks. In contrast, MobileNetV2 is designed to be lighter and more computationally efficient, making it ideal for deployment on mobile devices or in resource-constrained environments without significantly compromising performance. These characteristics make both models highly relevant for medical image analysis, particularly in brain tumor detection, which demands both accuracy and efficiency.This study uses a public dataset that has been preprocessed through augmentation and normalization. Both models were trained and evaluated using accuracy, loss, and confusion matrix metrics. The results show that MobileNetV2 achieved higher accuracy (97.8%) compared to Xception (94.9%) with a lower error rate. For precision, recall, and F1-score metrics, the results were identical up to four decimal places, further supporting that MobileNetV2 is more suitable for brain tumor detection in resource-limited settings. Based on the findings, MobileNetV2 demonstrates superior performance compared to Xception, making it the favorable choice.
Co-Authors Anik Nur Handayani