A Hierarchical Feature-Based Methodology to Perform Cervical Cancer Classification [electronic resource]

• Ngôn ngữ: eng
• Ký hiệu phân loại: 616.9945 Other diseases
• Thông tin xuất bản: Washington, D.C. : Oak Ridge, Tenn. : United States. Dept. of Energy. Office of Science ; Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy, 2021
• Mô tả vật lý: Size: Article No. 4091 : , digital, PDF file.
• Bộ sưu tập: Metadata
• ID: 259696
• Chủ đề: 1. 60 applied life sciences; 2. Cancer classification; 3. Cervical lesions; 4. Feature extraction; 5. Hierarchical model; 6. Image classification; 7. Learning algorithm; 8. Pap smear; 9. Random forest classifier

Tóm tắt

 Prevention of cervical cancer could be performed using Pap smear image analysis. This test screens pre-neoplastic changes in the cervical epithelial cells
  accurate screening can reduce deaths caused by the disease. Pap smear test analysis is exhaustive and repetitive work performed visually by a cytopathologist. This article proposes a workload-reducing algorithm for cervical cancer detection based on analysis of cell nuclei features within Pap smear images. We investigate eight traditional machine learning methods to perform a hierarchical classification. We propose a hierarchical classification methodology for computer-aided screening of cell lesions, which can recommend fields of view from the microscopy image based on the nuclei detection of cervical cells. We evaluate the performance of several algorithms against the Herlev and CRIC databases, using a varying number of classes during image classification. Results indicate that the hierarchical classification performed best when using Random Forest as the key classifier, particularly when compared with decision trees, k-NN, and the Ridge methods.