Colon Cancer Diagnosis by Efficient Artificial Intelligence Techniques and Multimodal Data
A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".
Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 2457
Special Issue Editors
Interests: artificial intelligence; deep learning; medical image analysis; histopathology; segmentation; classification
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Colonoscopy is regarded as a main and straightforward clinical diagnosis tool for colon cancer, with regular screening being a critical step in reducing mortality rates. Medical imaging techniques and histological analysis of hematoxylin and eosin (H&E) slides, in addition to this process, are still required for subtle colon cancer examinations. The goal of this Special Issue is to propose novel methods for colon cancer diagnosis using artificial intelligence (AI) and deep learning methodologies to analyze multi-modality medical images and clinical health records (EHRs). The following are just a few of the themes covered in this Special Issue:
- Colon cancer CAD systems;
- Efficient methods for colon cancer segmentation;
- Pathways of colorectal cancer development and progression;
- Colon cancer detection;
- Biomarkers for diagnosis and prognosis in colon cancer;
- Surgical innovations (latest technologies, and techniques).
Dr. Vivek Kumar Singh
Dr. Mohamed Abdel-Nasser
Guest Editors
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.
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Keywords
- colon cancer diagnosis
- medical images
- electronic health records
- deep learning
- artificial intelligence
- explainable AI models
- federated learning
- CAD systems
