Today, medical and health care is playing an increasingly important role in people’s lives, the number of medical images increases rapidly, and the traditional manual diagnosis can’t meet the increasing demand of clinical diagnosis. It is the core of this research to realize the localization and classification of lesions by training a neural network, and to assist doctors in clinical diagnosis. Based on the one-stage object detection network FCOS, this paper improves the detection accuracy to a level comparable to the two-stage detection network while taking into account the detection speed and achieves a mAP of 0.664 on the MRI test set, which is higher than most existing networks. Overall, the proposed network has good performance in clinical assistance, and can be competent for some clinical application tasks of real-time detection.
Underexposed images are usually low in brightness and contrast, which degrade the performance of many computer vision algorithms. To solve the problem of overexposing areas that tend to be normal while recovering dark areas in low-light image enhancement tasks, we propose an image-to-patch enhancement model and design a lightweight convolutional neural network called PatchNet. Specifically, the new enhancement model indirectly enhances the network by introducing a patch image, which preserves the incremental information from the low-light image to the normal image. The incremental information is fused with the input image to recover the dark areas while protecting the normal areas of the image. Extensive experiments on real datasets demonstrate the advantages of our method over state-of-the-art methods in subjective feeling and objective evaluation. Our method has achieved better results in restoring details and the adjustment of brightness. By comparing to other methods, our method is more efficient.
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