Innovative chest X-ray image recognition technique and its economic value

Image recognition techniques can recognize abnormal medical images, directly contributing to achieving high-quality diagnosis. In particular, with the development of deep learning technology, the recognition accuracy of abnormalities has steadily increased. However, there are usually no adequate learning instances for chest X-ray images, which directly leads to the failure of high-quality recognition. To solve this problem, we proposed a multi-weight-based limited learning instance model for chest X-ray image recognition. First, an optimized saliency detection model directly deleted the unsatisfactory learning instances, especially for learning instances without obvious significance. Second, multi-scale decomposition and sparse representation were combined to calculate the weights of different learning instances. Third, a multi-weight-based cost function was constructed to achieve high-quality recognition results by considering learning instances from multiple cases. Finally, according to the experimental database, we carried out experiments in which our method could achieve satisfactory recognition accuracy while using limited learning instances. More importantly, the economic value of this method cannot be underestimated considering that modern technology has become an important way to promote economic development.