ArticleJournal of healthcare engineering2022
Multispectral Image under Tissue Classification Algorithm in Screening of Cervical Cancer.
Article in Journal of healthcare engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed, 5 citations in OpenAlex.
- Current Trends in Diagnosis and Early Monitoring of Oral Cavity Cancer: Techniques and Biomarkers.Cancers · 2026Review
- Investigation of the clinical value of artificial intelligence-derived prognostic signature in cervical cancer based on machine learning algorithms.Discover oncology · 2026Article
- Research on the efficacy of hybrid deep learning models for image-based classification of common oral conditions.BMC oral health · 2026Article
- From Image-Guided Surgery to Computer-Assisted Real-Time Diagnosis with Hyperspectral and Multispectral Imaging: A Systematic Review in Gynecologic Oncology.Diagnostics (Basel, Switzerland) · 2026Review
- Feasibility study of hyperspectral colposcopy as a novel tool for detecting precancerous cervical lesions.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The objectives of this study were to improve the efficiency and accuracy of early clinical diagnosis of cervical cancer and to explore the application of tissue classification algorithm combined with multispectral imaging in screening of cervical cancer. 50 patients with suspected cervical cancer were selected. Firstly, the multispectral imaging technology was used to collect the multispectral images of the cervical tissues of 50 patients under the conventional white light waveband, the narrowband green light waveband, and the narrowband blue light waveband. Secondly, the collected multispectral images were fused, and then the tissue classification algorithm was used to segment the diseased area according to the difference between the cervical tissues without lesions and the cervical tissues with lesions. The difference in the contrast and other characteristics of the multiband spectrum fusion image would segment the diseased area, which was compared with the results of the disease examination. The average gradient, standard deviation (SD), and image entropy were adopted to evaluate the image quality, and the sensitivity and specificity were selected to evaluate the clinical application value of discussed method. The fused spectral image was compared with the image without lesions, it was found that there was a clear difference, and the fused multispectral image showed a contrast of 0.7549, which was also higher than that before fusion (0.4716), showing statistical difference (
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Registered trials
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