Exploration of cervical cancer image processing technology based on deep learning
Cheng Cheng, Yi Yang, Youshan Qu · 2024
The aim of this paper is to investigate cervical cancer image processing technology utilizing deep learning. Cervical cancer stands as a prevalent malignancy in females, and precise identification and localization of cancer cells hold paramount significance for treatment and prognosis evaluation. This paper presents the fundamental workflow of cervical cancer image processing and the associated principles of deep learning, including convolutional neural networks, autoencoders, and generative adversarial networks. In recent times, the swift advancement of deep learning technology has brought forth novel concepts and approaches for cervical cancer image processing. This paper is oriented toward the exploration of cervical cancer image processing technology grounded in deep learning. First, the basic workflow of cervical cancer image processing, including steps such as image acquisition, preprocessing, feature extraction, and target detection, is introduced. The application of deep learning in cervical cancer image processing is discussed in detail. As one of the core deep learning technologies, convolutional neural networks (CNNs) have achieved significant results in the fields of image classification, segmentation, and detection. This paper shall present the fundamental principles and prevalent architectures of CNNs, alongside their instances of utilization in cervical cancer image processing. Furthermore, the utilization of alternative deep learning approaches in cervical cancer image processing is also introduced. Subsequently, the paper contrasts the strengths and weaknesses of diverse deep learning techniques in cervical cancer image processing and deliberates the challenges and future trajectories of development within this domain.