Protocol non-adherence in teledermatology, particularly during image acquisition, such as missing rulers or incorrect image orientations, leads to invalid examinations, delayed diagnoses, and increased patient burdens. This study addresses these challenges by evaluating modern neural networks architectures for instance segmentation—Mask2Former (transformer-based) and YOLOv11 (hybrid CNN)— and proposing a novel orientation correction pipeline to improve adherence in two protocols: Approximation (identifying rulers/patient tags) and Panoramic (correcting body orientation errors). Using the Santa Catarina State Telemedicine System dataset (14,238 images for Approximation; 3,692 for Panoramic), models were benchmarked against Mask R-CNN measuring computational efficiency and precision metrics. Results demonstrate YOLOv11’s superiority, achieving state-of-the-art performance with 86.08% AP75, reducing segmentation errors by 14% compared to Mask R-CNN, while maintaining computational efficiency (1.3 GB VRAM, 566–699 ms latency). For panoramic protocol images, our post-processing pipeline mitigated orientation errors by methodically rotating misaligned human masks, improving weighted F-scores from 0.51 to 0.82 and significantly reducing misclassifications between valid and invalid poses. While Mask2Former’s transformer-based design architecture exhibited higher precision, the computational demands (11.7 GB VRAM) hindered deployability in low-resource clinics. The study concludes that hybrid models like YOLOv11 optimally balance segmentation accuracy and operational efficiency, offering actionable insights for real-world clinical implementation. This work bridges AI advancements with clinical pragmatism, offering a framework to enhance protocol adherence, reduce invalid examinations by addressing orientation inconsistencies, and optimize diagnostic workflows in resource-constrained teledermatology.