Lung cancer has a high death rate of around seven million cases every year worldwide. A computed tomography (CT) scan provides certain essential data concerning lung diseases and their diagnosis. The main objective of this work is to classify various lung diseases such as Normal, Bronchiectasis, and Pleural Effusion. The proposed approach consists of three stages, namely pre-processing, feature extraction, and classification. At first, CT lung images are collected from the dataset and pre-processed. After pre-processing, the important texture features are extracted from each image. For feature extraction, spiral-optimized Gabor filter (SOGF) is utilized. The proposed SOGF is a combination of spiral optimization algorithm (SOA) and Gabor filter (GF). Then, the extracted features are given to the convolutional neural network (CNN) to classify different types of lung diseases. For comparison, we use different classifiers, namely artificial neural network (ANN), Random Tree, and the Naïve Bayes. The experimental results show that our proposed approach attained the maximum accuracy of 93.67% which is high compared to other methods.

中文翻译：

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使用螺旋优化 Gabor 滤波器和卷积神经网络进行有效的肺部图像分类和检测

全球每年约有七百万例肺癌的高死亡率。计算机断层扫描 (CT) 扫描可提供有关肺部疾病及其诊断的某些基本数据。这项工作的主要目标是对各种肺部疾病进行分类，例如正常、支气管扩张和胸腔积液。该方法由三个阶段组成，即预处理、特征提取和分类。首先，从数据集中收集 CT 肺部图像并进行预处理。经过预处理后，从每幅图像中提取重要的纹理特征。对于特征提取，使用螺旋优化的 Gabor 滤波器（SOGF）。所提出的 SOGF 是螺旋优化算法（SOA）和 Gabor 滤波器（GF）的组合。然后，将提取的特征输入卷积神经网络（CNN）以对不同类型的肺部疾病进行分类。为了进行比较，我们使用不同的分类器，即人工神经网络（ANN）、随机树和朴素贝叶斯。实验结果表明，我们提出的方法达到了 93.67% 的最大准确率，与其他方法相比较高。