Yeganli, Seyedeh Faegheh

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Profile URL
https://gcris.yasar.edu.tr/handle/123456789/12751
Name Variants
Seyedeh Faegheh Yeganli
Job Title
Dr.Öğr.Üyesi
Email Address
Main Affiliation
01.01.09.01. Bilgisayar Mühendisliği Bölümü
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Former Staff
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ORCID ID
0000-0002-8230-0802
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WoS Researcher ID
AAM-5226-2021

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2022 Medical Technologies Congress TIPTEKNO 20221
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    Conference Object
    Fast and Interpretable Deep Learning Pipeline for Breast Cancer Recognition
    (Institute of Electrical and Electronics Engineers Inc., 2022) Mahdi Bonyani; Faezeh Yeganli; Seyedeh Faegheh Yeganli
    Breast cancer is one of the main causes of death across the world in women. Early diagnosis of this type of cancer is critical for treatment and patient care. In this paper we propose a fast and interpretable deep learning-based pipeline for automatic detection of the metastatic tissues in breast histopathological images. Firstly the proposed pipeline uses multiple pre-processing and data augmentation techniques to reduce over-fitting. Then the proposed pipeline employs one - cycle policy technique in the pre-trained convolutional neural networks model in shallow and deep fine-tuning phases to find the optimal values. Finally gradient-weighted class activation mapping (Grad-CAM) technique is utilized to produce a coarse localization map of the important regions in the image. Experiments on the PatchCamelyon dataset demonstrate the superior classification performance of the proposed method over the state-of-the-art. © 2022 Elsevier B.V. All rights reserved.