Dendritic Learning and Miss Region Detection-Based Deep Network for Multi-scale Medical Segmentation

Abstract

Automatic identification and segmentation of lesions in medical images has become a focus area for researchers. Segmentation for medical image provides professionals with a clearer and more detailed view by accurately identifying and isolating specific tissues, organs, or lesions from complex medical images, which is crucial for early diagnosis of diseases, treatment planning, and efficacy tracking. This paper introduces a deep network based on dendritic learning and missing region detection (DMNet), a new approach to medical image segmentation. DMNet combines a dendritic neuron model (DNM) with an improved SegNet framework to improve segmentation accuracy, especially in challenging tasks such as breast lesion and COVID-19 CT scan analysis. This work provides a new approach to medical image segmentation and confirms its effectiveness. Experiments have demonstrated that DMNet outperforms classic and latest methods in various performance metrics, proving its effectiveness and stability in medical image segmentation tasks.