YOLO-FMS: A Lightweight and Efficient Model for Medical Microscopic Smear Detection

Deep learning-based computer vision technology has been extensively utilized to assist medical microscopic smear diagnoses, such as complete blood count, detecting mycobacterium tuberculosis in sputum samples, and identifying plasmodium in blood smears. Effective medical microscopic smear detection necessitates balancing both detection accuracy and model parameters, facilitating the application of the model on low-resource computing platforms and ensuring real-time detection capabilities. Furthermore, the model should demonstrate superior generalization capabilities on microscopic smears to meet various detection tasks. In this study, we propose YOLO-FMS, a lightweight and efficient model based on YOLOv5 for medical microscopic smear detection with a compact weight of 15.06M, which can resolve the challenge of the balance between detection accuracy and model parameters. Firstly, YOLO-FMS improves the performance of small-scale platelet and mycobacterium tuberculosis detection by adding a small target detection head. Secondly, A lightweight convolutional technique, GSConv, was introduced to make the symbolic ability of the lightweight convolutional as close to the vanilla convolutional as possible and to reduce the computational cost. Thirdly, the feature extraction ability of YOLO-FMS is enhanced by C3-B-CBAM and Tiny-SPPCSPC modules constructed by our proposed method. Comprehensive experiments prove that YOLO-FMS shows high detection accuracy, with 92.5% mAP on the BCCD dataset and 87.6% mAP on the Tuberculosis-Phonecamera dataset. Additionally, numerous verification experiments are conducted on the BCDD and BBBC041 datasets, and the results confirm the effectiveness and generalization capability of YOLO-FMS in the medical microscopic smear detection field. The codes of YOLO-FMS are available at
https://github.com/GefionP/YOLO-FMS
.