UCTLFANet: a low-rank fine-tuning model for microalgae image segmentation.

The fine-tuning module optimizes the model's parameter structure while conserving computing resources. We introduce UCTLFANet, a low-rank fine-tuning model, for microalgae image segmentation. This model supports morphological analysis, cell counting, and physiological state assessment of microalgae. In this paper, we innovatively propose a low-rank fine-tuning method using a large language model for the image segmentation task of microalgae. This module employs the LoRA-FA method to adjust the model's original training weights through low-rank increments. After fine-tuning, the model performs segmentation tasks with minimal parameter updates, enhancing the efficiency and accuracy of microalgae segmentation. To explore this module's role further, we added it to the fully connected and convolutional layers of the UCTLFANet model during experiments and compared their performances. The results indicate that integrating this module into the fully connected layer yields the best outcomes. Compared to the UNet, UNet++, Attention-UNet, and UCTransNet models, our method demonstrates superior performance. Additionally, integrating the LoRA-FA module into the UNet, UNet++, and Attention-UNet models shows that UCTLFANet is more advanced and effective. Furthermore, the proposed model shows promising potential for generalization to other datasets or applications, making it a viable tool for broader marine microbial imaging detection.