An Optimized Object Detection Approach on Medical Image using Feature Enhancement and Dynamic Loss

Detection based on medical images is crucial for improving disease cure rates and patient prognosis. However, existing methods have limitations in feature extraction and computational efficiency. This paper presents an optimized method for medical image object detection using feature enhancement and dynamic loss FENet-UIoU . It combines receptive field attention convolution RFAConv , efficient up-sampling convolution block EUCB , large separable kernel attention LSKA with UIoU loss function to overcome the limitations of traditional convolutional neural network CNNs in medical image detection. RFAConv highlights tumor features through spatial attention mechanisms, EUCB improves feature map resolution and computational efficiency, LSKA enhances feature capture and expression, and unified intersection over union UIoU loss function uses dynamic weight allocation to optimize prediction box focus. Experimental results show that the proposed model achieves a performance improvement 7.8 on mean average precision mAP when adopting the proposed method in comparison with baseline. Meanwhile, ablation experiments verify the synergistic effect of each module, which shows that his study provides a high-precision and high-efficiency solution for medical image object detection.