Abstract
The accuracy of retinal vessel segmentation (RVS) is crucial in assisting physicians in the ophthalmology diagnosis or other systemic diseases. However, manual segmentation needs a high level of knowledge, time-consuming, complex, and prone to errors. As a result, automatic vessel segmentation is required, which might be a significant technological breakthrough in the medical field. We proposed a novel strategy in this paper, that uses neural architecture search (NAS) to optimize a U-net architecture using a binary teaching learning-based optimization (BTLBO) evolutionary algorithm for RVS to increase vessel segmentation performance and reduce the workload of manually developing deep networks with limited computing resources. We used a publicly available DRIVE dataset to examine the proposed approach and showed that the discovered model generated by the proposed approach outperforms existing methods.
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Rajesh, C., Kumar, S. (2023). Automatic Retinal Vessel Segmentation Using BTLBO. In: Thakur, M., Agnihotri, S., Rajpurohit, B.S., Pant, M., Deep, K., Nagar, A.K. (eds) Soft Computing for Problem Solving. Lecture Notes in Networks and Systems, vol 547. Springer, Singapore. https://doi.org/10.1007/978-981-19-6525-8_15
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