Editorial for Special Issue “Image Analysis and Machine Learning in Cancers”
Author:
Affiliation:
1. School for Advanced Digital Technology, Southern Alberta Institute of Technology (SAIT), Calgary, AB T2M 0L4, Canada
2. Department of Electronic Engineering, University of Rome Tor Vergata, 00133 Rome, Italy
Abstract
Publisher
MDPI AG
Link
https://www.mdpi.com/2072-6694/17/5/778/pdf
Reference11 articles.
1. (2025, January 02). Cancer Today—World Health Organization. Available online: https://gco.iarc.who.int/today/en.
2. AlGhamdi, R., Asar, T.O., Assiri, F.Y., Mansouri, R.A., and Ragab, M. (2023). Al-Biruni Earth Radius Optimization with Transfer Learning Based Histopathological Image Analysis for Lung and Colon Cancer Detection. Cancers, 15.
3. Klontzas, M.E., Koltsakis, E., Kalarakis, G., Trpkov, K., Papathomas, T., Karantanas, A.H., and Tzortzakakis, A. (2023). Machine Learning Integrating 99mTc Sestamibi SPECT/CT and Radiomics Data Achieves Optimal Characterization of Renal Oncocytic Tumors. Cancers, 15.
4. Alamgeer, M., Alruwais, N., Alshahrani, H.M., Mohamed, A., and Assiri, M. (2023). Dung Beetle Optimization with Deep Feature Fusion Model for Lung Cancer Detection and Classification. Cancers, 15.
5. Dzieniszewska, A., Garbat, P., and Piramidowicz, R. (2024). Improving Skin Lesion Segmentation with Self-Training. Cancers, 16.
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