Image processing plays a vital role in many areas such as healthcare, military, scientific and business due to its wide variety of advantages and applications. Detection of computed tomography (CT) liver disease is one of the difficult tasks in the medical field. Hand crafted features and classifications are the two types of methods used in the previous approaches, to classify liver disease. But these classification results are not optimal. In this article, we propose a novel method utilizing deep belief network (DBN) with grasshopper optimization algorithm (GOA) for liver disease classification. Initially, the image quality is enhanced by preprocessing techniques and then features like texture, color and shape are extracted. The extracted features are reduced by utilizing the dimensionality reduction method like principal component analysis (PCA). Here, the DBN parameters are optimized using GOA for recognizing liver disease. The experiments are performed on the real time and open source CT image datasets which embraces normal, cyst, hepatoma, and cavernous hemangiomas, fatty liver, metastasis, cirrhosis, and tumor samples. The proposed method yields 98% accuracy, 95.82% sensitivity, 97.52% specificity, 98.53% precision, and 96.8% F‐1 score in simulation process when compared with other existing techniques.

中文翻译：

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使用蚱蜢算法优化深度置信网络参数进行肝病分类

由于其广泛的优势和应用，图像处理在医疗保健、军事、科学和商业等许多领域发挥着至关重要的作用。计算机断层扫描 (CT) 肝脏疾病的检测是医学领域的一项艰巨任务。手工制作的特征和分类是先前方法中使用的两种方法，用于对肝脏疾病进行分类。但是这些分类结果并不是最优的。在本文中，我们提出了一种利用深度置信网络 (DBN) 和蚱蜢优化算法 (GOA) 进行肝脏疾病分类的新方法。首先，通过预处理技术提高图像质量，然后提取纹理、颜色和形状等特征。通过使用诸如主成分分析（PCA）之类的降维方法来减少提取的特征。在这里，DBN 参数使用 GOA 进行优化以识别肝脏疾病。这些实验是在实时和开源 CT 图像数据集上进行的，这些数据集包括正常、囊肿、肝癌和海绵状血管瘤、脂肪肝、转移瘤、肝硬化和肿瘤样本。与其他现有技术相比，所提出的方法在模拟过程中产生了 98% 的准确度、95.82% 的灵敏度、97.52% 的特异性、98.53% 的精确度和 96.8% 的 F-1 分数。