Fingerprint image enhancement is one of the most crucial steps in an automated fingerprint identification system. In this paper, an effective algorithm for fingerprint image quality improvement is proposed. The algorithm consists of two stages. The first stage is decomposing the input fingerprint image into four subbands by applying two-dimensional discrete wavelet transform. At the second stage, the compensated image is produced by adaptively obtaining the compensation coefficient for each subband based on the referred Gaussian template. The experimental results indicated that the compensated image quality was higher than that of the original image. The proposed algorithm can improve the clarity and continuity of ridge structures in a fingerprint image. Therefore, it can achieve higher fingerprint classification rates than related methods can.

Currently, cardiovascular disease affects a relatively high proportion of the world's population. Thus, developing simple and effective methods for monitoring patients with cardiovascular disease is critical for research. Monitoring the heart rate of

Breast cancer has a high mortality rate among cancers. If the type of breast tumor can be correctly diagnosed at an early stage, the survival rate of the patients will be greatly improved. Considering the actual clinical needs, the classification model of breast pathology images needs to have the ability to make a correct classification, even in facing image data with different characteristics. The existing convolutional neural network (CNN)-based models for the classification of breast tumor pathology images lack the requisite generalization capability to maintain high accuracy when confronted with pathology images of varied characteristics. Consequently, this study introduces a new classification model, STMLAN (Single-Task Meta Learning with Auxiliary Network), which integrates Meta Learning and an auxiliary network. Single-Task Meta Learning was proposed to endow the model with generalization ability, and the auxiliary network was used to enhance the feature characteristics of breast pathology images. The experimental results demonstrate that the STMLAN model proposed in this study improves accuracy by at least 1.85% in challenging multi-classification tasks compared to the existing methods. Furthermore, the Silhouette Score corresponding to the features learned by the model has increased by 31.85%, reflecting that the proposed model can learn more discriminative features, and the generalization ability of the overall model is also improved.

This study proposes a new CycleGAN-based stain transfer model, called S3CGAN, equipped with a specialized color classifier structure. The specialized color classifier can assist the generative network to conquer the existing challenge in GANs, namely the instability of the network caused by the insufficient representativeness of the training data in the initial stage of network training. The color classifier is pretrained, hence it can provide correct color information feedback to the generator during the initial network training phase. The augmented information from color classification enables the generator to generate superior results. Owing to the CycleGAN architecture, the proposed model does not require representative paired inputs. The proposed model uses U-Net and a Markovian discriminator to enhance the structural retention ability to generate images with high fidelity.