医学
四分位间距
阶段(地层学)
置信区间
召回
放射科
可靠性(半导体)
外科
内科学
语言学
量子力学
生物
物理
哲学
古生物学
功率(物理)
作者
Sara Moccia,Elena De Momi,Marco Guarnaschelli,Matteo Savazzi,Andrea Laborai
出处
期刊:Journal of medical imaging
[SPIE - International Society for Optical Engineering]
日期:2017-09-29
卷期号:4 (03): 1-1
被引量:66
标识
DOI:10.1117/1.jmi.4.3.034502
摘要
Early stage diagnosis of laryngeal squamous cell carcinoma (SCC) is of primary importance for lowering patient mortality or after treatment morbidity. Despite the challenges in diagnosis reported in the clinical literature, few efforts have been invested in computer-assisted diagnosis. The objective of this paper is to investigate the use of texture-based machine-learning algorithms for early stage cancerous laryngeal tissue classification. To estimate the classification reliability, a measure of confidence is also exploited. From the endoscopic videos of 33 patients affected by SCC, a well-balanced dataset of 1320 patches, relative to four laryngeal tissue classes, was extracted. With the best performing feature, the achieved median classification recall was 93% [interquartile range (IQR)=6%]. When excluding low-confidence patches, the achieved median recall was increased to 98% (IQR=5%), proving the high reliability of the proposed approach. This research represents an important advancement in the state-of-the-art computer-assisted laryngeal diagnosis, and the results are a promising step toward a helpful endoscope-integrated processing system to support early stage diagnosis.
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