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Comparison of Tuberculosis Bacteria Classification from Digital Image of Sputum Smears

机译:从痰涂片数字图像比较结核菌的分类

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Tuberculosis is caused by Mycrobacterium tuberculosis which is known to be the most deadly infection disease in the world. Most of death cases caused by Tuberculosis occur in countries with low income. Tuberculosis can be identified using microscopic analysis by examining sputum sample from the suspected tuberculosis patient. Positive and negative tuberculosis was determined by the amount of bacteria found in sputum. Microscopic analysis is known to have weaknesses in distinguishing between tuberculosis bacterias. Also in counting the number of bacteria seen in the microscope because the tendency of it to accumulate together. In addition, manual counting of tuberculosis bacteria takes a lot of time, require high concentration and labor-intensive. We provide automated systems to distinguish between single and multiple tuberculosis bacteria and non-bacterial ones. The compared methods are backpropagation and K-nearest neighbor (KNN). Sputum sample digital images are converted to HSV color channels. The bacterial length and bacterial endpoint is a feature that is extracted from tuberculosis bacteria. This unique feature is used to classify which one is belong to single bacteria and which one is included as double bacteria. Based on the experimental results, both methods can be used to classify single bacteria and double bacteria with 93.22% accuracy for backpropagation and 94.92% for KNN. So K- NN method better than backpropagation method for classifying tuberculosis bacteria.
机译:结核是由结核分枝杆菌引起的,结核分枝杆菌是世界上最致命的感染疾病。结核病造成的大多数死亡病例都发生在低收入国家。可以通过检查疑似结核病患者的痰液样本使用显微镜分析来鉴定结核病。阳性和阴性结核病由痰液中发现的细菌数量决定。已知显微分析在区分结核菌方面有弱点。另外,在计数显微镜下看到的细菌数时,还因为细菌会聚集在一起的趋势。另外,结核菌的人工计数需要很多时间,需要高浓度和劳动强度。我们提供自动化系统,以区分单个和多个结核菌和非细菌菌。比较的方法是反向传播和K最近邻(KNN)。痰液样本数字图像将转换为HSV颜色通道。细菌的长度和终点是从结核菌中提取的特征。此独特功能用于分类哪个细菌属于单一细菌,哪个细菌属于双重细菌。根据实验结果,两种方法都可以用于对单个细菌和双重细菌进行分类,反向传播的准确度为93.22%,KNN的准确度为94.92%。因此,K-NN方法比反向传播方法更好地对结核菌进行分类。

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