The early diagnosis of colorectal cancer (CRC) traditionally leverages upon the microscopic examination of histological slides by experienced pathologists, which is very time-consuming and rises many issues about the reliability of the results. In this paper we propose using Convolutional Neural Networks (CNNs), a class of deep networks that are successfully used in many contexts of pattern recognition, to automatically distinguish the cancerous tissues from either healthy or benign lesions. For this purpose, we designed and compared different CNN-based classification frameworks, involving either training CNNs from scratch on three classes of colorectal images, or transfer learning from a different classification problem. While a CNN trained from scratch obtained very good (about 90%) classification accuracy in our tests, the same CNN model pre-trained on the ImageNet dataset obtained even better accuracy (around 96%) on the same testing samples, requiring much lesser computational resources.

Going Deeper into Colorectal Cancer Histopathology / Ponzio, Francesco; Macii, Enrico; Ficarra, Elisa; DI CATALDO, Santa - In: International Joint Conference on Biomedical Engineering Systems and TechnologiesELETTRONICO. - [s.l] : springer, 2019. - ISBN 9783030291952. - pp. 114-131 [10.1007/978-3-030-29196-9_7]

Going Deeper into Colorectal Cancer Histopathology

Ponzio Francesco;Macii Enrico;Ficarra Elisa;Di Cataldo Santa
2019

Abstract

The early diagnosis of colorectal cancer (CRC) traditionally leverages upon the microscopic examination of histological slides by experienced pathologists, which is very time-consuming and rises many issues about the reliability of the results. In this paper we propose using Convolutional Neural Networks (CNNs), a class of deep networks that are successfully used in many contexts of pattern recognition, to automatically distinguish the cancerous tissues from either healthy or benign lesions. For this purpose, we designed and compared different CNN-based classification frameworks, involving either training CNNs from scratch on three classes of colorectal images, or transfer learning from a different classification problem. While a CNN trained from scratch obtained very good (about 90%) classification accuracy in our tests, the same CNN model pre-trained on the ImageNet dataset obtained even better accuracy (around 96%) on the same testing samples, requiring much lesser computational resources.
9783030291952
International Joint Conference on Biomedical Engineering Systems and Technologies
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2752172