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Aryan Safakish
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Predicting head and neck cancer treatment outcomes with pre-treatment quantitative ultrasound texture features and optimising machine learning classifiers with texture-of …
A Safakish, L Sannachi, D DiCenzo, C Kolios, A Pejović-Milić, ...
Frontiers in Oncology 13, 1258970, 2023
72023
Predicting head and neck cancer treatment outcomes using textural feature level fusion of quantitative ultrasound spectroscopic and computed tomography: A machine learning approach
A Moslemi, A Safakish, L Sannchi, D Alberico, S Halstead, G Czarnota
2023 IEEE International Ultrasonics Symposium (IUS), 1-4, 2023
32023
Deep Texture Analysis—Enhancing CT Radiomics Features for Prediction of Head and Neck Cancer Treatment Outcomes: A Machine Learning Approach
A Safakish, L Sannachi, A Moslemi, A Pejović-Milić, GJ Czarnota
Radiation 4 (1), 50-68, 2024
22024
Deep Texture Analysis Enhanced MRI Radiomics for Predicting Head and Neck Cancer Treatment Outcomes with Machine Learning Classifiers
A Safakish, A Moslemi, D Moore-Palhares, L Sannachi, I Poon, I Karam, ...
Radiation 4 (2), 192-212, 2024
2024
Results: It was found that a 7-feature multivariable model of QUS texture features using a support vector machine (SVM) classifier demonstrated 81% sensitivity, 76% specificity …
A Reznik, J Rodgers, S Shukla, O Bubon, GJ Czarnota, A Safakish, ...
2023
Investigating The Effects Of Pulsed Radiofrequency Therapy In The Blocking Of Action Potentials In Nerves
A Safakish
Toronto Metropolitan University, 2020
2020
Predicting Head & Neck Cancer Treatment Outcomes with Quantitative Ultrasound Texture Features & Optimizing Machine Learning Classifiers with Novel Texture-of-Texture Features
A Safakish, L Sannachi, D DiCenzo, C Kolios, A Pejović-Milić, ...
Toronto Metropolitan University, 0
Enhancing CT Radiomics Features for Prediction of Head and Neck Cancer Treatment Outcomes: A Machine Learning Approach
A Safakish, L Sannachi, A Moslemi, A Pejović-Milić, GJ Czarnota
Toronto Metropolitan University, 0
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