Advanced Brain Tumor Segmentation Using EMCAD: Efficient Multi-scale Convolutional Attention Decoding

Abstract--Brain tumor segmentation is a critical pre-processing step in the medical image analysis pipeline that involves precise delineation of tumor regions from healthy brain tissue in medical imaging data, particularly MRI scans. An efficient and effective decoding mechanism is crucial in brain tumor segmentation especially in scenarios with limited computational resources. However these decoding mechanisms usually come with high computational costs. T o address this concern EMCAD a new efficient multi-scale convolutional attention decoder designed was utilized to optimize both performance and computational efficiency for brain tumor segmentation on the BraTs2020 dataset consisting of MRI scans from 369 brain tumor patients. The preliminary result obtained by the model achieved a best Dice score of 0.31 and maintained a stable mean Dice score of 0.285 0.015 throughout the training process which is moderate. The initial model maintained consistent performance across the validation set without showing signs of over-fitting. A. Medical Image Segmentation Medical image segmentation is a crucial process in medical image analysis that involves partitioning medical images into multiple meaningful segments or regions, each corresponding to different anatomical structures, tissues, or pathologies [1]. This computational technique has evolved significantly with the advent of deep learning approaches, enabling automatic delineation of regions of interest from various imaging modalities such as MRI, CT, and ultrasound [2]. The segmentation process helps in extracting quantitative information from medical images, which is essential for diagnosis, treatment planning, and follow-up assessment [3].