Department of Computer Science and Engineering, College of Engineering Muttathara, Thiruvananthapuram, Kerala, India.
Department of Computer Science and Engineering, Mar Baselios College of Engineering and Technology, Thiruvananthapuram, Kerala, India.
J Xray Sci Technol. 2023;31(4):699-711. doi: 10.3233/XST-230028.
Tuberculosis (TB) is a highly infectious disease that mainly affects the human lungs. The gold standard for TB diagnosis is Xpert Mycobacterium tuberculosis/ resistance to rifampicin (MTB/RIF) testing. X-ray, a relatively inexpensive and widely used imaging modality, can be employed as an alternative for early diagnosis of the disease. Computer-aided techniques can be used to assist radiologists in interpreting X-ray images, which can improve the ease and accuracy of diagnosis.
To develop a computer-aided technique for the diagnosis of TB from X-ray images using deep learning techniques.
This research paper presents a novel approach for TB diagnosis from X-ray using deep learning methods. The proposed method uses an ensemble of two pre-trained neural networks, namely EfficientnetB0 and Densenet201, for feature extraction. The features extracted using two CNNs are expected to generate more accurate and representative features than a single CNN. A custom-built artificial neural network (ANN) called PatternNet with two hidden layers is utilized to classify the extracted features.
The effectiveness of the proposed method was assessed on two publicly accessible datasets, namely the Montgomery and Shenzhen datasets. The Montgomery dataset comprises 138 X-ray images, while the Shenzhen dataset has 662 X-ray images. The method was further evaluated after combining both datasets. The method performed exceptionally well on all three datasets, achieving high Area Under the Curve (AUC) scores of 0.9978, 0.9836, and 0.9914, respectively, using a 10-fold cross-validation technique.
The experiments performed in this study prove the effectiveness of features extracted using EfficientnetB0 and Densenet201 in combination with PatternNet classifier in the diagnosis of tuberculosis from X-ray images.
结核病（TB）是一种高度传染性疾病，主要影响人体肺部。TB 诊断的金标准是 Xpert 分枝杆菌/利福平耐药（MTB/RIF）检测。X 射线是一种相对廉价且广泛使用的成像方式，可作为疾病早期诊断的替代方法。计算机辅助技术可用于辅助放射科医生解读 X 射线图像，从而提高诊断的便捷性和准确性。
利用深度学习技术开发一种基于 X 射线图像的 TB 诊断计算机辅助技术。
本研究提出了一种基于深度学习方法从 X 射线图像中诊断 TB 的新方法。该方法使用两个预先训练好的神经网络（即 EfficientnetB0 和 Densenet201）的集合进行特征提取。与单个 CNN 相比，这两个 CNN 提取的特征有望生成更准确和有代表性的特征。使用一个名为 PatternNet 的自定义人工神经网络（ANN），具有两个隐藏层，用于对提取的特征进行分类。
在两个公共可访问的数据集，即 Montgomery 和 Shenzhen 数据集上评估了所提出方法的有效性。Montgomery 数据集包含 138 张 X 射线图像，而 Shenzhen 数据集包含 662 张 X 射线图像。在合并两个数据集后，对该方法进行了进一步评估。该方法在所有三个数据集上的表现都非常出色，使用 10 折交叉验证技术分别获得了 0.9978、0.9836 和 0.9914 的高曲线下面积（AUC）评分。
本研究中的实验证明了在 X 射线图像中使用 EfficientnetB0 和 Densenet201 提取的特征与 PatternNet 分类器相结合在结核病诊断中的有效性。
