卷积神经网络在结核病检测中的应用

Mehmet BABALIK, Çiğdem BAKIR
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引用次数: 0

摘要

结核病是一种由结核分枝杆菌引起的传染病。结核病仍然是世界范围内一个重要的公共卫生问题,在发展中国家尤为普遍。这种呼吸系统疾病会引起严重的症状,尤其是对肺部的影响。结核病的症状包括长时间咳嗽、呼吸短促、胸痛、虚弱、发烧、盗汗和不适。该疾病的诊断是通过临床症状以及生物医学成像方法和实验室检查做出的。这些成像方式包括x射线、计算机断层扫描(CT)和磁共振成像(MRI)等技术。结核病的早期诊断对于治疗和预防疾病的传播具有重要意义。利用深度学习方法对结核病生物医学图像进行分类,可以加快诊断过程,提高准确性,更有效地指导治疗。在本研究中,它旨在成为利用深度学习对结核病进行分类的重要一步。详细展示了生成的CNN网络、参数值、使用的层数、验证数据得到的复杂度矩阵、准确率和损失图。在我们的研究中,通过使用与文献中使用的神经网络不同的网络结构来提高成功率。所提出的CNN模型的成功率约为98%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detection of Tuberculosis Disease with Convolutional Neural Networks
Tuberculosis is an infectious disease caused by bacteria called Mycobacterium tuberculosis. Tuberculosis is still an important public health problem worldwide and is common especially in developing countries. This respiratory disease can cause serious symptoms, especially affecting the lungs. Symptoms of tuberculosis include prolonged coughing, shortness of breath, chest pain, weakness, fever, night sweats, and malaise. The diagnosis of the disease is made by clinical signs as well as biomedical imaging methods and laboratory tests. These imaging modalities include techniques such as x-rays, computed tomography (CT), and magnetic resonance imaging (MRI). Early diagnosis of tuberculosis disease is of great importance in terms of treatment and prevention of the spread of the disease. The use of deep learning methods to classify biomedical images of tuberculosis disease can accelerate the diagnosis process, increase accuracy and guide treatment more effectively. In this study, it aims to be an important step in the classification of tuberculosis disease with deep learning. The generated CNN network, parameter values, layers used, complexity matrices obtained for verification data, accuracy and loss graphs are shown in detail. In our study, the success rate was increased by using a different network structure than the neural networks used in the literature. Approximately 98% success was achieved with the proposed CNN model.
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