Výsledky vyhledávání - "Sajjad Ejtehadifar"

Applications of machine-learning algorithms for prediction of benign and malignant breast lesions using ultrasound radiomics signatures: A multi-center study

Autor: Hassan Homayoun, Wai Yee Chan, Taha Yusuf Kuzan, Wai Ling Leong, Kübra Murzoglu Altintoprak, Afshin Mohammadi, Anushya Vijayananthan, Kartini Rahmat, Sook Sam Leong, Mohammad Mirza-Aghazadeh-Attari, Sajjad Ejtehadifar, Fariborz Faeghi, U. Rajendra Acharya, Ali Abbasian Ardakani

Publikováno v: Biocybernetics and Biomedical Engineering. 42:921-933

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::9a5ef5dc2632c6fddced28b73c60d8d3
https://doi.org/10.1016/j.bbe.2022.07.004

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Artificial intelligence, BI-RADS evaluation and morphometry: A novel combination to diagnose breast cancer using ultrasonography, results from multi-center cohorts

Autor: Hessam Hamyoon, Wai Yee Chan, Afshin Mohammadi, Taha Yusuf Kuzan, Mohammad Mirza-Aghazadeh-Attari, Wai Ling Leong, Kübra Murzoglu Altintoprak, Anushya Vijayananthan, Kartini Rahmat, Nazimah Ab Mumin, Sook Sam Leong, Sajjad Ejtehadifar, Fariborz Faeghi, Jamileh Abolghasemi, Edward J. Ciaccio, U. Rajendra Acharya, Ali Abbasian Ardakani

Publikováno v: European journal of radiology. 157

To develop and validate a machine learning (ML) model for the classification of breast lesions on ultrasound images.In the present study, three separate data cohorts containing 1288 breast lesions from three countries (Malaysia, Iran, and Turkey) wer

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e09dbd1eeb4ea7e11b22da9acd5a85a2
https://pubmed.ncbi.nlm.nih.gov/36356463

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Accurate automated diagnosis of carpal tunnel syndrome using radiomics features with ultrasound images: A comparison with radiologists' assessment

Autor: Mohammad Mirza-Aghazadeh-Attari, U. Rajendra Acharya, Sajjad Ejtehadifar, Fariborz Faeghi, Ali Abbasian Ardakani, Afshin Mohammadi, Jamileh Abolghasemi

Publikováno v: European journal of radiology. 136

Purpose Ultrasonography is the most common imaging modality used to diagnose carpal tunnel syndrome (CTS). Recently artificial intelligence algorithms have been used to diagnose musculoskeletal diseases accurately without human errors using medical i

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ae09746f7cf09553b73ce4cabca0f582
https://pubmed.ncbi.nlm.nih.gov/33434859

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