Probabilistic Linguistic Convolutional Neural Network Dealing With Low-Quality Image Classification

Abstract

With the advent of the smart medical era, medical images play a central role in clinical diagnosis and treatment. However, the existence of low-quality medical images has significantly impacted the progress of smart medicine. In order to solve the problems of noise, fuzziness, and insufficient contrast faced by traditional convolutional neural networks when processing low-quality medical images, this article innovatively adopts the probabilistic linguistic term sets to characterize the fuzzy degree of the image, and proposes a probabilistic linguistic convolutional neural network (PL-CNN) which fuses probabilistic linguistic information. For this new PL-CNN, we provide a complete calculation process including forward propagation, backward propagation, and parameter update. Finally, we apply the PL-CNN to the classification of CIFAR-10 series datasets and breast datasets, demonstrating the versatility and effectiveness of the proposed method. This article not only provides a universal deep-learning method for image classification, but also offers a new idea and method for the processing of low-quality medical images in smart healthcare.