A deep learning detection method for pancreatic cystic neoplasm based on Mamba architecture

Objective: Early diagnosis of pancreatic cystic neoplasm (PCN) is crucial for patient survival. This study proposes M-YOLO, a novel model combining Mamba architecture and YOLO, to enhance the detection of pancreatic cystic tumors. The model addresses the technical challenge posed by the tumors’ complex morphological features in medical images. Methods: This study develops an innovative deep learning network architecture, M-YOLO (Mamba YOLOv10), which combines the advantages of Mamba and YOLOv10 and aims to improve the accuracy and efficiency of pancreatic cystic neoplasm(PCN) detection. The Mamba architecture, with its superior sequence modeling capabilities, is ideally suited for processing the rich contextual information contained in medical images. At the same time, YOLOv10's fast object detection feature ensures the system's viability for application in clinical practice. Results: M-YOLO has a high sensitivity of 0.98, a specificity of 0.92, a precision of 0.96, an F1 value of 0.97, an accuracy of 0.93, as well as a mean average precision (mAP) of 0.96 at 50% intersection-to-union (IoU) threshold on the dataset provided by Changhai Hospital. Conclusions: M-YOLO(Mamba YOLOv10) enhances the identification performance of PCN by integrating the deep feature extraction capability of Mamba and the fast localization technique of YOLOv10.