Development of new diagnostic methods for lung cancer based on computer analysis of ultrasonic B-mode images

利用计算机分析肺癌病例支气管镜检查获得的b线图像，对肺良恶性病变进行鉴别诊断。结果与相同用途的人类提交进行了比较。采用中间层64个单元、50万/ 100万次学习重复和中间层128个单元、50万/ 100万次学习重复的人工神经网络诊断准确率为71.0%。65.4%。74.8%。72.0%，学习重复次数差异无统计学意义。此外，具有16年、5年和2年经验的外科医生的诊断准确率分别为69.2%、56.1%和63.6%。人工神经网络的诊断准确率高于外科医生。利用人工神经网络进行计算机图像分析被认为是一种很有前途的研究方法。

Differential diagnosis between benign and malignant lung lesions was made using computer analysis of B-mode images obtained by bronchoscopy for lung cancer cases. The results were compared with human lodgment for the same uses. Diagnostic accuracy of ANN using 64 units in the middle layer and 500,000/1,000,000 learning repetitions and 128 units in the middle layer and 500,000/1,000,000 learning repetitions are 71.0%. 65.4%. 74.8%. 72.0%, no significant difference was found among numbers of learning repetitions. In addition, diagnostic accuracy for the surgeon with 16, 5 and 2 years of experience are 69.2%, 56.1% and 63.6%. Diagnostic accuracy of ANN is better than that of the surgeon. Computer images analysis by ANN was regarded as promising methods for the future study.