التفاصيل البيبلوغرافية
| العنوان: |
A Brief Survey on Breast Cancer Diagnostic With Deep Learning Schemes Using Multi-Image Modalities |
| المؤلفون: |
Tariq Mahmood, Jianqiang Li, Yan Pei, Faheem Akhtar, Azhar Imran, Khalil Ur Rehman |
| المصدر: |
IEEE Access, Vol 8, Pp 165779-165809 (2020) |
| بيانات النشر: |
IEEE, 2020. |
| سنة النشر: |
2020 |
| المجموعة: |
LCC:Electrical engineering. Electronics. Nuclear engineering |
| مصطلحات موضوعية: |
Breast cancer, computer-aided-diagnosis, deep learning techniques, medical image analysis, lesions classification, segmentation, Electrical engineering. Electronics. Nuclear engineering, TK1-9971 |
| الوصف: |
Patients with breast cancer are prone to serious health-related complications with higher mortality. The primary reason might be a misinterpretation of radiologists in recognizing suspicious lesions due to technical issues in imaging qualities and heterogeneous breast densities which increases the false-(positive and negative) ratio. Early intervention is significant in establishing an up-to-date prognosis process which can successfully mitigate complications of disease with higher recovery. The manual screening of breast abnormalities through traditional machine learning schemes misinterpret the inconsistent feature-extraction process which poses a problem, i.e., patients being called-back for biopsies to eliminates the suspicions. However, several deep learning-based methods have been developed for reliable breast cancer prognosis and classification but very few of them provided a comprehensive overview of lesions segmentation. This research focusses on providing benefits and risks of breast multi-imaging modalities, segmentation schemes, feature extraction, classification of breast abnormalities through state-of-the-art deep learning approaches. This research also explores various well-known databases using ”Breast Cancer” keyword to present a comprehensive survey on existing diagnostic schemes to open-up new research challenges for radiologists and researchers to intervene as early as possible to develop an efficient and reliable breast cancer prognosis system using prominent deep learning schemes. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
2169-3536 |
| Relation: |
https://ieeexplore.ieee.org/document/9184879/; https://doaj.org/toc/2169-3536 |
| DOI: |
10.1109/ACCESS.2020.3021343 |
| URL الوصول: |
https://doaj.org/article/9635df62e9624dda87e7cd6ae01958fa |
| رقم الانضمام: |
edsdoj.9635df62e9624dda87e7cd6ae01958fa |
| قاعدة البيانات: |
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