Brain-RetinaNet: Detection of Brain Tumour Using an Improved RetinaNet in Magnetic Resonance Imaging

Abstract

Brain tumours disrupt the normal functioning of the brain and, if left untreated, can invade surrounding tissues, blood vessels, and nerves, posing a severe threat. Consequently, early detection is crucial to prevent tragic outcomes. Distinguishing brain tumours through manual detection poses a significant challenge given their diverse features, such as differing shapes, sizes, and nucleus characteristics. Therefore, this research introduces an improved architecture for tumour detection named as Brain- RetinaNet, an extension of the RetinaNet model. Brain-RetinaNet is specifically designed for automated detection and iden-tification of brain tumours in MRI images. It utilises an advanced multiscale feature fusion mechanism within the X-module, complemented by the channel attention module. The feature fusion mechanism within the X-module progressively merges features from different scales, producing enriched feature maps that encompass valuable information. At the same time, the attention module dynamically allocates optimal weights to individual channels within the feature map, enabling the network to prioritise relevant features while reducing interference from unnecessary ones. Moreover, this study employs data augmentation technique to address the limitation of a limited number of available samples. Experimental results indicate that Brain-RetinaNet outperforms existing detectors such as YOLO, SSD, Centernet, EfficientNet, and M2det for the brain tumour detection from MRI images.

Keywords

deep learning / image classification / RetinaNet

Acknowledgements

The authors extend their appreciation to King Saud University, Saudi Arabia for funding this work through Ongoing Research Funding Pro-gram, (ORF-2025-704), King Saud University, Riyadh, Saudi Arabia.

Funding

The authors extend their appreciation to King Saud University, Saudi Arabia for funding this work through Ongoing Research Funding Pro-gram, (ORF-2025-704), King Saud University, Riyadh, Saudi Arabia.

Conflicts of Interest

The authors declare no confiicts of interest.

Data Availability Statement

The dataset used for the research is available publicly, and the request can be made to authors for further assistance.

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