This paper addresses multiple critical issues in the sole publicly available dataset for nematode image classification—the I-Nema dataset—including data leakage, scale markers in the lower-right corners, inconsistent image preprocessing, uneven brightness distribution, and pronounced background noise that significantly degrade model generalization. To resolve these, we apply (1) triple-hash deduplication (aHash, pHash, and dHash); (2) manual cropping to remove scale markers; (3) unified image preprocessing; (4) unified image normalization with histogram stretching to enforce Gaussian-distributed brightness; and (5) RMBG-2.0-based background removal. Ablation studies demonstrate the impact of dataset issues on model accuracy. Post-repair accuracy decreases to 61.56% but better reflects the model’s generalization capability. Furthermore, we propose Mask-guided Image Alignment for Occlusion-minimized Mix (MIAOMix), a novel augmentation method to strengthen the regularization effect. This approach leverages image masks generated by RMBG-2.0 background segmentation to locate the minimum-overlap region via crop-and-slide operations, followed by background-excluded linear interpolation. Evaluated on ResNet-50 architecture, MIAOMix achieves a notably improved accuracy of 73.51% ± 1.82% (without label smoothing) and 73.31% ± 1.48% (with label smoothing = 0.2) on the optimized I-Nema dataset, outperforming Mixup and its variants (SaliencyMix, CutMix) by an average margin of 3.26% under identical training configurations.