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The proposed model configurations yield better results in more complex cases with a not significant or small bump in processing time. Experimental results demonstrate that our best-proposed solution achieves a Dice score of up to 97.89% compared to the baseline U-Net model's Dice score of 94.50%. Statistical, visual, and computational properties of each convolutional neural network architecture are addressed.The diagnosis of autoimmune polyglandular syndrome (APS) types 1/2 is difficult due to their rarity and nonspecific clinical manif
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