Brain Tumor Detection in MRI Images Using Artificial Intelligence with a Transfer Learning Approach and Decision Threshold Optimization

Brain Tumor Detection in MRI Images Using Artificial Intelligence with a Transfer Learning Approach and Decision Threshold Optimization | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 5 January 2026 V1 Latest version Share on Brain Tumor Detection in MRI Images Using Artificial Intelligence with a Transfer Learning Approach and Decision Threshold Optimization Author : Benyamin Mozafarnaserabad 0009-0003-8849-426X [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176764592.24290238/v1 123 views 69 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Timely and accurate detection of brain tumors in MRI images is one of the most critical challenges in diagnostic radiology. This study develops a binary image classification model (tumor/healthy) using the lightweight and efficient MobileNetV2 architecture combined with a transfer learning strategy. Considering the clinical importance of minimizing false negatives (FN), two key innovations were introduced to enhance diagnostic safety: first, applying asymmetric class weighting (a weight of 1.5 for the tumor class) during the training phase to impose a stronger penalty on FN errors; and second, optimizing the decision threshold from 0.5 to 0.3 during the evaluation phase. The final evaluation on the test set (203 images) demonstrated the effectiveness of this approach: at the 0.3 threshold, the model achieved 100% recall for the tumor class, indicating a complete elimination of false negatives (FN=0). This result ensures the highest level of sensitivity and diagnostic safety required for computer-aided detection (CAD) systems in medical screening. In essence, this research proposes an innovative and efficient approach for developing a model capable of faster and more accurate detection of brain tumors. Supplementary Material File (en-co.pdf) Download 485.99 KB Information & Authors Information Version history V1 Version 1 05 January 2026 Copyright This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License Keywords "artificial intelligence in medicine bioengineering brain tumor detection " deep learning transfer learning Authors Affiliations Benyamin Mozafarnaserabad 0009-0003-8849-426X [email protected] Undergraduate Student of Electrical Engineering, Lorestan University View all articles by this author Metrics & Citations Metrics Article Usage 123 views 69 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Benyamin Mozafarnaserabad. Brain Tumor Detection in MRI Images Using Artificial Intelligence with a Transfer Learning Approach and Decision Threshold Optimization. Authorea . 05 January 2026. 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