Technical Research on Cervical Cancer Screening Using Spectral Analysis-based Artificial Intelligence Algorithms

The document presents a technical research study on cervical cancer screening using spectral analysis-based artificial intelligence algorithms. It emphasizes the importance of prevention and treatment during the reversible stage known as Cervical Intraepithelial Neoplasia (CIN). The document highlights the low screening rates, especially in rural areas in China, despite the increasing incidence of cervical cancer. Various treatment methods for cervical cancer, including surgical treatment, chemotherapy, and radiotherapy, are discussed. Efficacy evaluation methods such as gynecological examinations, imaging tests, and tumor markers are also explored. The second part of the document focuses on the principles and implementation of artificial intelligence algorithms for cervical cancer diagnosis. It specifically discusses the Support Vector Machines (SVM) and Backpropagation Neural Networks (BPNN) algorithms. The principles of SVM, including basic concepts, separating hyperplanes, and kernel functions, are explained. The principles of BPNN, which simulate the transmission and processing of neural signals in biological systems, are also discussed. Lastly, the document emphasizes the importance of data augmentation to address class imbalance issues and increase the size of the training set. It describes the multi-class classification strategy used for classifying cervical precancerous lesions, employing both SVM and BPNN algorithms. Overall, the document provides technical research and insights into cervical cancer screening using spectral analysis-based artificial intelligence algorithms.