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首页> 外文会议>East Indonesia Conference on Computer and Information Technology >Lung X-Ray Image Enhancement to Identify Pneumonia with CNN
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Lung X-Ray Image Enhancement to Identify Pneumonia with CNN

机译：肺X射线图像增强识别CNN的肺炎

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摘要

Chest x-rays have various values of intensity and high contrast. Chest X-rays require a contrast stretching process so that the image can be analyzed and diagnosed correctly. Image contrast improvement can be based on the histogram value of the image intensity. Pneumonia can be diagnosed by taking chest X-rays. Diagnosis of Pneumonia based on chest x-rays can be done automatically by a computer. Computer-based Pneumonia diagnosis requires a reliable and accurate algorithm. A reliable and accurate algorithm, namely Convolutional Neural Network. This research aimed to prove whether the chest X-ray image that was performed by contrast improvement had a significant effect in diagnosing Pneumonia. The algorithm proposed in diagnosing Pneumonia is a Convolutional Neural Network. The CLAHE repaired chest X-ray image was trained with 8 CNN architectural models. The training results of the eight CNN architectural models respectively have a loss function value of 0.0057, 0.028, 0.0964, 0.0446, 0.0473, 0.0573, 0.0979, 0.1407. The results of diagnostic testing for Pneumonia in the eight CNN architectural models were 79.65%, 79.01%, 80.29%, 76.92%, 82.53%, 80.45%, 79.81%, 78.04%, respectively. The highest accuracy result when testing is 82.53% with the CNN 35 Layers architectural model, with a description of the input image is grayscale with a size of 224x224.
机译：胸部X射线具有各种强度和高对比度的值。胸部X射线需要对比度拉伸过程，从而可以正确地分析和诊断图像。图像对比度改善可以基于图像强度的直方图值。通过胸部X射线可以诊断肺炎。基于胸部X射线的肺炎的诊断可以由计算机自动完成。基于计算机的肺炎诊断需要一种可靠和准确的算法。一种可靠和准确的算法，即卷积神经网络。该研究旨在证明通过对比改善进行的胸X射线图像是否对诊断肺炎具有显着影响。在诊断肺炎时提出的算法是卷积神经网络。 Clahe修复了胸部X射线图像培训，有8个CNN建筑模型。八CNN建筑模型的培训结果分别具有0.0057,0.028,0.0964,0.0446,0.0473,0.0573,0.0979,0.1407的损耗函数值。八CNN建筑模型肺炎诊断检测结果分别为79.65％，79.01％，76.01％，76.92％，82.53％，80.45％，79.81％，78.04％。使用CNN 35层架构模型测试时，测试的最高精度结果是82.53％，输入图像的描述是灰度，尺寸为224x224。

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