An efficient colorectal cancer detection network using atrous convolution with coordinate attention transformer and histopathological images.

Autor: Khalid M; Department of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura University, 21955, Makkah, Saudi Arabia., Deivasigamani S; Department of Computer Science and Engineering, University College of Engineering, A Constituent College of Anna University, Thirukkuvalai, Chennai, India., V S; Department of Computer Science and Engineering, Panimalar Engineering College, Chennai, 600123, India., Rajendran S; Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, 602105, Tamil Nadu, India. surendran.phd.it@gmail.com.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Aug 17; Vol. 14 (1), pp. 19109. Date of Electronic Publication: 2024 Aug 17.
DOI: 10.1038/s41598-024-70117-y
Abstrakt: The second most common type of malignant tumor worldwide is colorectal cancer. Histopathology image analysis offers crucial data for the clinical diagnosis of colorectal cancer. Currently, deep learning techniques are applied to enhance cancer classification and tumor localization in histopathological image analysis. Moreover, traditional deep learning techniques might loss integrated information in the image while evaluating thousands of patches recovered from whole slide images (WSIs). This research proposes a novel colorectal cancer detection network (CCDNet) that combines coordinate attention transformer with atrous convolution. CCDNet first denoises the input histopathological image using a Wiener based Midpoint weighted non-local means filter (WMW-NLM) for guaranteeing precise diagnoses and maintain image features. Also, a novel atrous convolution with coordinate attention transformer (AConvCAT) is introduced, which successfully combines the advantages of two networks to classify colorectal tissue at various scales by capturing local and global information. Further, coordinate attention model is integrated with a Cross-shaped window (CrSWin) transformer for capturing tiny changes in colorectal tissue from multiple angles. The proposed CCDNet achieved accuracy rates of 98.61% and 98.96%, on the colorectal histological image and NCT-CRC-HE-100 K datasets correspondingly. The comparison analysis demonstrates that the suggested framework performed better than the most advanced methods already in use. In hospitals, clinicians can use the proposed CCDNet to verify the diagnosis.
(© 2024. The Author(s).)
Databáze: MEDLINE
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