Research Article Assessing Deep Learning Techniques for the Recognition of Tropical Disease in Images from Parasitological Exams Ammar Akram Abdulrazzaq, 1 Asaad T. Al-Douri, 2 Abdulsattar Abdullah Hamad , 3,4 Mustafa Musa Jaber, 5 and Zelalem Meraf 6 1 Department of Medical Laboratory Technologies, Al-Maarif University College, Ramadi, Iraq 2 Department of Dental Industry, College of Medical Technology, Al-Kitab University, Alton Kopru, Iraq 3 Department of Medical Laboratory Techniques, Dijlah University College, Baghdad 10021, Iraq 4 Department of Medical Laboratory Techniques, Al-Turath University College, Baghdad 10021, Iraq 5 Department of Medical Instruments Engineering Techniques, Al-Farahidi University, Baghdad 10021, Iraq 6 Department of Statistics, Injibara University, Injibara, Ethiopia Correspondence should be addressed to Zelalem Meraf; zelalemmeraf@inu.edu.et Received 6 April 2022; Revised 23 April 2022; Accepted 27 April 2022; Published 9 May 2022 Academic Editor: Sivakumar Pandian Copyright © 2022 Ammar Akram Abdulrazzaq et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Schistosoma mansoni 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. e current process for diagnosis still depends on an analysis of parasitological exams performed by a human being under a laboratory microscope. e 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). e 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. 1. Introduction Schistosomiasis is one of the most endemic neglected tropical diseases in the world, according to the World Health Organization (WHO) [1]. Schistosomiasis is present in 76 countries across Africa, the Eastern Mediterranean, the Western Pacific, and the Americas. It is estimated that 200 million people are infected with the disease and 500 million people are exposed to risk areas [2]. In the Arabic region, the estimated number of individuals infected with schistoso- miasis is 2.5 million people, but this number can reach 6.5 million due to lack of diagnosis or mapping of some endemic regions [3, 4]. ere is still a lack of research that carries out mapping more endemic regions in the country. e most recent research was carried out using georeferenced data by field researchers to map the prevalence of schistosomiasis in the northeast region, more specifically in the coastal region of the state of Schaap et al. [4]. According to the London Declaration’s definitions, the fight against schistosomiasis, according to the definitions of the London Declaration [5], involves the search for new techniques for diagnosis and mapping of the disease, which are easy to use, reliable, and low cost for patient monitoring and identification. With this, the aim is to observe the emerging behavior of this endemic. Computational techniques and new clinical analysis tech- nologies are already widely used to support decision-making in the medical field and detect various diseases in medical image examinations. Schistosoma mansoni [6] is an infection Hindawi Bioinorganic Chemistry and Applications Volume 2022, Article ID 2682287, 8 pages https://doi.org/10.1155/2022/2682287