QAIS-DSNN: Tumor Area Segmentation of MRI Image with Optimized Quantum Matched-Filter Technique and Deep Spiking Neural Network

Table 2

The comparison of the proposed approach with previous ones in terms of Dice evaluation criteria.

Tumor improvement or ET areas	Tumor nucleus or TC	Total tumor or WT	Method	Reference
81.84%	88.34%	91.2%	ADNN-PSO	Irfan Sharif et al. [32]
85.83%	79.72%	90.21%	3D cascaded CNN-TTA	Wang et al. [27]
79.19%	85.40%	90.31%	Cascaded CNN	Wang et al. [27]
77.07%	73.04%	89.56%	Multiclass WNet+TTA	Wang et al. [27]
71.78%	74.81%	88.24%	MCCNN	Hu et al. [28]
72.29%	76.75%	86.23%	Two-stage	Zhou et al. [26]
70.9%	75.1%	85.1%	Ordinary fusion	Zhou et al. [26]
73.44%	76.58%	86.38%	3D UNet	Zhou et al. [26]
72.55%	75%	84.94%	APFNet	Zhou et al. [26]
74.43%	76.88%	86.56%	APF+3D-CRF	Zhou et al. [26]
74.50%	80.15%	91.92%	QAIS-DSNN	Proposed approach