Akram Abdulrazzaq Ammar, Al-Douri Asaad T, Abdullah Hamad Abdulsattar, Musa Jaber Mustafa, Meraf Zelalem
Department of Medical Laboratory Technologies, Al-Maarif University College, Ramadi, Iraq.
Department of Dental Industry, College of Medical Technology, Al-Kitab University, Alton Kopru, Iraq.
Bioinorg Chem Appl. 2022 May 9;2022:2682287. doi: 10.1155/2022/2682287. eCollection 2022.
is one of the tropical diseases with the greatest epidemic reach in the world. One of the WHO guidelines is the prior and efficient diagnosis for mapping foci and applying the appropriate treatment of infected people. The current process for diagnosis still depends on an analysis of parasitological exams performed by a human being under a laboratory microscope. The area of pattern recognition in images presents itself as a promising alternative to support and automate image-based exams, and deep learning techniques have been successfully applied for this purpose. In order to automate this process, it is proposed in this work the application of deep learning methods for the detection of schistosomiasis eggs, and a comparison is made between two deep learning techniques, convolutional neural network (CNN) and structured pyramidal neural network (SPNN). The results obtained in a real database indicate that the techniques are effective in the recognition of schistosomiasis eggs, in which both obtained AUC (area under the curve) above 0.90, with the CNN showing superiority in this aspect. . However, the SPNN proved to be faster than the CNN.
是世界上流行范围最广的热带疾病之一。世界卫生组织的指导方针之一是进行前期有效诊断，以绘制疫源地并对感染者进行适当治疗。目前的诊断过程仍然依赖于由人员在实验室显微镜下对寄生虫学检查进行分析。图像中的模式识别领域是支持和自动化基于图像的检查的一个有前途的替代方法，深度学习技术已成功应用于此目的。为了使这个过程自动化，本文提出应用深度学习方法检测血吸虫卵，并对卷积神经网络（CNN）和结构化金字塔神经网络（SPNN）这两种深度学习技术进行比较。在真实数据库中获得的结果表明，这些技术在识别血吸虫卵方面是有效的，两者的曲线下面积（AUC）均高于0.90，其中CNN在这方面表现出优势。然而，事实证明SPNN比CNN更快。
