Gastric cancer detection based on Colorectal Cancer transfer learning

• Type: Article in International Conference Proceedings Book
• Date: 2023

Authors

Nóbrega, S (Author) Other; Neto, A (Author) Other; Coimbra, M (Author) FCUP; Cunha, A (Author) Other

Conference proceedings International

Title: 2023 IEEE 7TH PORTUGUESE MEETING ON BIOENGINEERING, ENBENG

Pages: 72-75

IEEE 7th Portuguese Meeting on Bioengineering (ENBENG)

Porto, PORTUGAL, JUN 22-23, 2023

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Other information

Authenticus ID: P-00Y-V40

DOI: 10.1109/enbeng58165.2023.10175323

Abstract (EN): Gastric Cancer (GC) and Colorectal Cancer (CRC) are some of the most common cancers in the world. The most common diagnostic methods are upper endoscopy and biopsy. Possible expert distractions can lead to late diagnosis. GC is a less studied malignancy than CRC, leading to scarce public data that difficult the use of AI detection methods, unlike CRC where public data are available. Considering that CRC endoscopic images present some similarities with GC, a CRC Transfer Learning approach could be used to improve AI GC detectors. This paper evaluates a novel Transfer Learning approach for real-time GC detection, using a YOLOv4 model pre-trained on CRC detection. The results achieved are promising since GC detection improved relatively to the traditional Transfer Learning strategy. © 2023 IEEE.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 3