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Machine learning for interpretation and analysis of microscopic images

Grant number: 20/12017-9
Support Opportunities:Scholarships in Brazil - Post-Doctoral
Start date: October 01, 2020
End date: January 31, 2024
Field of knowledge:Interdisciplinary Subjects
Principal Investigator:Helder Takashi Imoto Nakaya
Grantee:Mauro César Cafundó de Morais
Host Institution:Instituto Israelita de Ensino e Pesquisa Albert Einstein (IIEPAE). São Paulo , SP, Brazil
Associated research grant:18/14933-2 - Integrative biology applied to human health, AP.JP2

Abstract

The present work plan aims to extract phenotypic features of the parasite species Trypanosoma, Plasmodium and Leishmania in images derived from microscopy. Such features consist of curvature attributes (minimum, maximum, average, standard deviation, variance, entropy and potential energy), geometric (area, circularity, proportion, perimeter, maximum and minimum distances); and texture statistics (contrast, momentum, second angular momentum, homogeneity, entropy and correlation). These attributes will then be selected through Principal Component Analysis (PCA). Subsequently, the attributes best classified in this way will be used in a geometric approach classification algorithm - Support Vector Machine (SVM). Such a method has the advantage of being computationally cheaper, as it does not require a Graphics Processing Unit (GPU). In this way, we can establish a new, faster and more accurate diagnostic approach to diseases caused by these parasites. (AU)

News published in Agência FAPESP Newsletter about the scholarship:
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Scientific publications
(The scientific publications listed on this page originate from the Web of Science or SciELO databases. Their authors have cited FAPESP grant or fellowship project numbers awarded to Principal Investigators or Fellowship Recipients, whether or not they are among the authors. This information is collected automatically and retrieved directly from those bibliometric databases.)
DE LIMA, VINICIO RODRIGUES; DE MORAIS, MAURO CESAR CAFUNDO; KIRCHGATTER, KARIN. Integrating artificial intelligence and wing geometric morphometry to automate mosquito classification. Acta Tropica, v. 249, p. 7-pg., . (20/12017-9)
CAFUNDO MORAIS, MAURO CESAR; SILVA, DIOGO; MILAGRE, MATHEUS MARQUES; DE OLIVEIRA, MAYKON TAVARES; PEREIRA, THAIS; SILVA, JOAO SANTANA; COSTA, LUCIANO DA F.; MINOPRIO, PAOLA; CESAR JUNIOR, ROBERTO MARCONDES; GAZZINELLI, RICARDO; et al. Automatic detection of the parasite Trypanosoma cruzi in blood smears using a machine learning approach applied to mobile phone images. PeerJ, v. 10, p. 19-pg., . (20/12017-9, 18/14933-2)