Assessing Deep Learning Techniques for the Recognition of Tropical Disease in Images from Parasitological Exams

Abstract

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. 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.