Banca de DEFESA: FERNANDO HUMBERTO DE ALMEIDA MORAES NETO

Uma banca de DEFESA de MESTRADO foi cadastrada pelo programa.
DISCENTE : FERNANDO HUMBERTO DE ALMEIDA MORAES NETO
DATA : 19/02/2020
HORA: 10:00
LOCAL: Auditório do Instituto de Matemática e Estatística
TÍTULO:

Convolutional neural network ensemble for mamography classification

PALAVRAS-CHAVES:

Convolutional neural network, Transfer learning, model ensemble, cancer.

PÁGINAS: 90
GRANDE ÁREA: Ciências Exatas e da Terra
ÁREA: Probabilidade e Estatística
SUBÁREA: Probabilidade e Estatística Aplicadas
RESUMO:

Cancer is the name given to a set of more than 100 diseases that have in common the disordered growth of cells that invade tissues and organs, according to the INCA – Instituto Nacional de Câncer. In 2018, approximately 18 million new cancer cases were diagnosed worldwide. Among these, breast cancer is the one that most affects women worldwide. In Brazil, between 2018 and 2019, breast cancer incidence estimates are 59,700 new cases, with 29.5% of cancers in women, according to the INCA. One way to diagnose breast cancer is by capturing radiographic images(mammograms) for patients at high risk. Mammography is able to identify suspicious changes in cancer before the onset of symptoms and its analysis is done by radiologists, who check for the existence of breast cancer. One way to help these professionals to classify mammography images is to use some computational techniques. Diagnosis based on computational techniques can help the doctor make a more accurate decision.
A computational technique widely used to analyze images are the CNN - Convolutional Neural Networks, its use can improve the diagnosis of breast cancer by helping radiologists and doctors. This work has the general objective of demonstrating the benefits of implementing CNN’s for cancer prediction in radiographic data.
For this, a database provided by the oncology hospital ACCamargo, which specializes in the diagnosis, treatment and research of cancer, was used. A combination of models with transfer of learning was used to classify these images obtaining an ACC of 84.66%.

MEMBROS DA BANCA:
Presidente - 2420269 - RICARDO FERREIRA DA ROCHA
Externo à Instituição - FRANCISCO LOUZADA NETO
Externo à Instituição - VINICIUS FERNANDO CALSALVARA