Abstract
Chagas disease is a widely spreaded illness caused by the parasite Trypanosoma cruzi (T. cruzi). Most cases go unnoticed until the accumulated myocardial damage affect the patient. The endomyocardium biopsy is a tool to evaluate sustained myocardial damage, but analyzing histopathological images takes a lot of time and its prone to human error, given its subjective nature. The following work presents a deep learning method to detect T. cruzi amastigotes on histopathological images taken from a endomyocardium biopsy during an experimental murine model. A U-Net convolutional neural network architecture was implemented and trained from the ground up. An accuracy of 99.19% and Jaccard index of 49.43% were achieved. The obtained results suggest that the proposed approach can be useful for amastigotes detection in histopathological images.Clinical relevance- The proposed method can be incorporated as automatic detection tool of amastigotes nests, it can be useful for the Chagas disease analysis and diagnosis.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.