REVISTA
AI

   
Redirigiendo al acceso original de articulo en 15 segundos...
Inicio  /  AI  /  Vol: 5 Par: 1 (2024)  /  Artículo
ARTÍCULO
TITULO

Convolutional Neural Networks in the Diagnosis of Colon Adenocarcinoma

Marco Leo    
Pierluigi Carcagnì    
Luca Signore    
Francesco Corcione    
Giulio Benincasa    
Mikko O. Laukkanen and Cosimo Distante    

Resumen

Colorectal cancer is one of the most lethal cancers because of late diagnosis and challenges in the selection of therapy options. The histopathological diagnosis of colon adenocarcinoma is hindered by poor reproducibility and a lack of standard examination protocols required for appropriate treatment decisions. In the current study, using state-of-the-art approaches on benchmark datasets, we analyzed different architectures and ensembling strategies to develop the most efficient network combinations to improve binary and ternary classification. We propose an innovative two-stage pipeline approach to diagnose colon adenocarcinoma grading from histological images in a similar manner to a pathologist. The glandular regions were first segmented by a transformer architecture with subsequent classification using a convolutional neural network (CNN) ensemble, which markedly improved the learning efficiency and shortened the learning time. Moreover, we prepared and published a dataset for clinical validation of the developed artificial neural network, which suggested the discovery of novel histological phenotypic alterations in adenocarcinoma sections that could have prognostic value. Therefore, AI could markedly improve the reproducibility, efficiency, and accuracy of colon cancer diagnosis, which are required for precision medicine to personalize the treatment of cancer patients.

 Artículos similares