Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran; Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran.
Dept. of Information Technology, Dr.Mahalingam College of Engg. & Tech., Pollachi, 642003, India.
Comput Biol Med. 2022 May;144:105392. doi: 10.1016/j.compbiomed.2022.105392. Epub 2022 Mar 10.
Cervical cancer is one of the most common types of cancer for women. Early and accurate diagnosis can save the patient's life. Pap smear testing is nowadays commonly used to diagnose cervical cancer. The type, structure and size of the cervical cells in pap smears images are major factors which are used by specialist doctors to diagnosis abnormality. Various image processing-based approaches have been proposed to acquire pap smear images and diagnose cervical cancer in pap smears images. Accuracy is usually the primary objective in evaluating the performance of these systems. In this paper, a two-stage method for pap smear image classification is presented. The aim of the first stage is to extract texture information of the cytoplasm and nucleolus jointly. For this purpose, the pap smear image is first segmented using the appropriate threshold. Then, a texture descriptor is proposed titled modified uniform local ternary patterns (MULTP), to describe the local textural features. Secondly, an optimized multi-layer feed-forward neural network is used to classify the pap smear images. The proposed deep neural network is optimized using genetic algorithm in terms of number of hidden layers and hidden nodes. In this respect, an innovative chromosome representation and cross-over process is proposed to handle these parameters. The performance of the proposed method is evaluated on the Herlev database and compared with many other efficient methods in this scope under the same validation conditions. The results show that the detection accuracy of the proposed method is higher than the compared methods. Insensitivity to image rotation is one of the major advantages of the proposed method. Results show that the proposed method has the capability to be used in online problems because of low run time. The proposed texture descriptor, MULTP is a general operator which can be used in many computer vision problems to describe texture properties of image. Also, the proposed optimization algorithm can be used in deep-networks to improve performance.
宫颈癌是女性最常见的癌症类型之一。早期和准确的诊断可以挽救患者的生命。巴氏涂片检查现在常用于诊断宫颈癌。巴氏涂片图像中的宫颈细胞的类型、结构和大小是专家医生用于诊断异常的主要因素。已经提出了各种基于图像处理的方法来获取巴氏涂片图像并诊断巴氏涂片图像中的宫颈癌。准确性通常是评估这些系统性能的主要目标。本文提出了一种用于巴氏涂片图像分类的两阶段方法。第一阶段的目的是联合提取细胞质和核仁的纹理信息。为此，首先使用适当的阈值对巴氏涂片图像进行分割。然后，提出了一种名为改进的均匀局部三元模式（MULTP）的纹理描述符来描述局部纹理特征。其次，使用优化的多层前馈神经网络对巴氏涂片图像进行分类。所提出的深度神经网络使用遗传算法在隐藏层和隐藏节点的数量方面进行了优化。在这方面，提出了一种创新的染色体表示和交叉过程来处理这些参数。在所提出的方法的性能是在 Herlev 数据库上评估的，并在相同的验证条件下与该范围内的许多其他有效方法进行了比较。结果表明，所提出的方法的检测精度高于比较方法。对图像旋转不敏感是所提出方法的主要优点之一。结果表明，由于运行时间短，所提出的方法具有在在线问题中使用的能力。所提出的纹理描述符 MULTP 是一种通用算子，可用于许多计算机视觉问题来描述图像的纹理特性。此外，所提出的优化算法可用于深度网络以提高性能。
