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Densely convolutional networks for breast cancer classification with multi-modal image fusion
Joint Authors
Badawi, Usamah
Zaghlul, Muhammad
Hamdi, Iman
Source
The International Arab Journal of Information Technology
Issue
Vol. 19, Issue 3A (s) (31 May. 2022), pp.463-469, 7 p.
Publisher
Zarqa University Deanship of Scientific Research
Publication Date
2022-05-31
Country of Publication
Jordan
No. of Pages
7
Main Subjects
Information Technology and Computer Science
Abstract EN
Breast cancer is the main health burden worldwide.
Cancer is located in the breast, starts when the cell grows under control and begins as in-situ carcinoma and when spread into other parts known as invasive carcinoma.
Breast cancer mass can early be found by image modality when discovering mass early can easily diagnose and treated.
Multimodalities used for the classification of breast cancer Such as mammography, ultrasound, and Magnetic resonance imaging.
Two types of fusion are used earlier fusion and later fusion.
Early fusion it’s a simple relation between modalities while later fusion gives more interest to fusion strategy to learn the complex relationship between various modalities as a result, can get highly accurate results when using the later fusion.
When combining two image modalities (mammography, ultrasound) and using an excel sheet containing the age, view, side, and status attribute associated with each mammographic image using DenseNet 201 with Layer level fusion strategy as later fusion by making connections between the various paths and same path by using Concatenated layer.
Fusing at the feature level achieves the best performance in terms of several evaluation metrics (accuracy, recall, precision area under the curve, and F1 score) and performance.
American Psychological Association (APA)
Hamdi, Iman& Badawi, Usamah& Zaghlul, Muhammad. 2022. Densely convolutional networks for breast cancer classification with multi-modal image fusion. The International Arab Journal of Information Technology،Vol. 19, no. 3A (s), pp.463-469.
https://search.emarefa.net/detail/BIM-1437120
Modern Language Association (MLA)
Hamdi, Iman…[et al.]. Densely convolutional networks for breast cancer classification with multi-modal image fusion. The International Arab Journal of Information Technology Vol. 19, no. 3A (Special issue) (2022), pp.463-469.
https://search.emarefa.net/detail/BIM-1437120
American Medical Association (AMA)
Hamdi, Iman& Badawi, Usamah& Zaghlul, Muhammad. Densely convolutional networks for breast cancer classification with multi-modal image fusion. The International Arab Journal of Information Technology. 2022. Vol. 19, no. 3A (s), pp.463-469.
https://search.emarefa.net/detail/BIM-1437120
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 468-469
Record ID
BIM-1437120