An Hybrid Lightweight Model for Brain Tumor Detection

Notsa Jeff Rakotozafy, Andriamasinoro Rahajaniaina and Adolphe Andriamanga Ratiarison
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Notsa Jeff Rakotozafy: Department of Mathematics, Computer Science and Applications, University of Toamasina, Toamasina
Andriamasinoro Rahajaniaina: Department of Mathematics, Computer Science and Applications, University of Toamasina, Toamasina
Adolphe Andriamanga Ratiarison: Department of Physics and Applications, University of Antananarivo, Antananarivo

International Journal of Research and Innovation in Applied Science, 2025, vol. 10, issue 9, 878-889

Abstract: In the last decade, deep transfer learning (TL) approaches are most widely used to detect and classify brain tumours imagines. However, current models are either complex and require significant computer resources, or they are lightweight but use a small dataset. To overcome these problems, in this paper we suggested a hybrid lightweight model for brain tumor detection in MRI images dataset efficiently and accurately. Our model used MobileNetV3Small as backbone followed by a single conv layer (the neck) to adjust the channel count and YOLO11 as detection component. So, YOLO11's inference time remains slower than that of MobileNetV3Small. The main difficulty lies in YOLO11's feature extractor, which, while performant, requires significant resources, limiting its use on mobile devices. To reduce complexity and improve efficiency on mobile devices, the intermediate multi-scale head of YOLO11 (CSP/upsample fusions) is removed as it is complex. The goal is to combine the strengths of each model. We conducted a comparative study between YOLO11 standard version and our model using the same dataset, hyper parameters and metrics. After more experiment, the proposed model has a higher result than YOLO11 in all metrics. It achieved 99.4% as mAP@50 and 99.8% as precision. These results have shown that our framework is both resilient, reliable and could run on the low resource environment. For future work, we plan to explore additional architectural optimizations and extend validation to larger, multi-institutional datasets. Further development will focus on using this model in new datasets.

Date: 2025
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