Parasitic Egg detection from Microscopic images using Convolutional Neural Networks

Abstract

The most common test for parasitic infection diagnosis is stool parasitological testing. The use of the Kato-Katz method in the preparation of slides for the development of the image bank discussed here was extremely important. Other authors' studies on the same topic were discussed. Various parasite eggs of various species were created. Then the binary and multiclass classifier architectures were empirically defined, and each model was implemented. The performance of the classifiers was evaluated using metrics recommended in the literature for both empirically defined and transfer learning architectures. Finally, experiments were conducted to improve the system's performance by allowing binary and multiclass models to communicate. This data was used to build a database of 66 parasite egg photos from various species. Using data augmentation techniques, a total of 48,000 photos were collected. The examined measures all reached 99.9%. Despite some species' eggs sharing morphological similarities, the second method correctly classified each egg with a 99.9% hit rate. The problems addressed received a 99.9% rating on the evaluation measures used to assess them. The method can also be applied to a larger number of helminth species and detection technologies using the same procedures.