TY  - JOUR
AU  - Popat, Mayuri 
AU  - Patel, Sanskruti 
PY  - 2026
TI  - Cascaded MNetV3UNet: A Lightweight Two-Stage   Architecture for High-Precision Brain Tumor Segmentation in MRI
JF  - Journal of Computer Science
VL  - 22
IS  - 2
DO  - 10.3844/jcssp.2026.589.604
UR  - https://thescipub.com/abstract/jcssp.2026.589.604
AB  - Precise brain tumor segmentation is an essential but quite challenging process in MRI images because of their irregular shapes, heterogeneous appearance, and low contrast with surrounding tissues. While U-Net-based architectures have achieved significant success, their high computational complexity limits deployment on resource-constrained systems. In this study, a Novel two-stage cascaded architecture, MNetV3UNet, is introduced, which employs the light-weight MobileNetV3-Large as an encoder and the standard U-Net as the decoder. MobileNetV3 block consists of a sequence of blocks that generate feature maps enhanced using Inverted Residual Blocks and squeeze-and-excitation modules. The Unet decoder consists of an iterative process of upsampling, interpolation, concatenation, and refinement. The first stage produces a coarse segmentation, which is refined in the second stage to enhance boundary accuracy and detail. The cascaded approach leverages a multi-scale feature extraction process, which is also coupled with the progressive refinement method. This way, we ensure a much higher degree of segmentation precision while still maintaining computational efficiency. The proposed model has been tested on the BraTS 2020 dataset, yielding a Dice score of 88.35 for Whole Tumor (WT), 89.03 for Tumor Core (TC), and 92.30% for Enhancing Tumor (ET). Additionally, it achieved a Jaccard score of 83.76 for WT, 86.15% for TC, and 89.68% for ET. The specificity obtained for WT was 99.72, for TC, 99.89, and for ET, 97.96%. It achieved sensitivity of 88.91 for WT, 89.37 for TC, and 92.88% for ET.  These outcomes provide clear evidence of the proposed innovative architecture's ability to achieve an excellent balance between segmentation accuracy and computational efficiency.