Efficient Breast Cancer Classification Using Histopathological Images and a Simple VGG

Autor:	Marcelo Luis Rodrigues Filho, Omar Andres Carmona Cortes
Jazyk:	angličtina
Rok vydání:	2022
Předmět:	breast cancer machine learning General Computer Science convolutional neural network histopathological images
Zdroj:	Revista de Informática Teórica e Aplicada; Vol. 29 No. 1 (2022); 102-114 Revista de Informática Teórica e Aplicada; v. 29 n. 1 (2022); 102-114
ISSN:	0103-4308 2175-2745
Popis:	Breast cancer is the second most deadly disease worldwide. This severe condition led to 627,000 people dying in 2018. Thus, early detection is critical for improving the patients' lifetime or even curing them. In this context, we can appeal to Medicine 4.0, which exploits machine learning capabilities to obtain a faster and more efficient diagnosis. Therefore, this work aims to apply a simpler convolutional neural network, called VGG-7, for classifying breast cancer in histopathological images. Results have shown that VGG-7 overcomes the performance of VGG-16 and VGG-19, showing an accuracy of 98%, a precision of 99%, a recall of 98%, and an F1 score of 98%.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bd8aca93da7222ca294fcb826fa9454d https://seer.ufrgs.br/index.php/rita/article/view/RITA_Vol29_Nr1_102 Zobrazit plný text záznamu