Improved breast cancer detection using fusion of bimodal sonographic features through binary firefly algorithm

Sasikala, S.; Kumar, S. Arun; Ezhilarasi, M.

doi:10.1080/13682199.2023.2164944

Cited by 1 publication

(2 citation statements)

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“…Based on this feature fusion concept, deep features derived from different breast imaging modalities could be a better choice for the future research direction. 44,45 Conclusion DL approaches for improving the detection performance of BC is analyzed in this study. This review focuses on the use of various DL architectures such as CNN, TL, cross-modal learning, fine-tuning CNN for CAD of BC.…”

Section: Inference From the Reviewmentioning

confidence: 99%

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Review on Deep Learning-Based CAD Systems for Breast Cancer Diagnosis

Kumar¹,

Sasikala²

2023

Technol Cancer Res Treat

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Breast Cancer (BC) is a major health issue in women of the age group above 45. Identification of BC at an earlier stage is important to reduce the mortality rate. Image-based noninvasive methods are used for early detection and for providing appropriate treatment. Computer-Aided Diagnosis (CAD) schemes can support radiologists in making correct decisions. Computational intelligence paradigms such as Machine Learning (ML) and Deep Learning (DL) have been used in the recent past in CAD systems to accelerate diagnosis. ML techniques are feature driven and require a high amount of domain expertise. However, DL approaches make decisions directly from the image. The current advancement in DL approaches for early diagnosis of BC is the motivation behind this review. This article throws light on various types of CAD approaches used in BC detection and diagnosis. A survey on DL, Transfer Learning, and DL-based CAD approaches for the diagnosis of BC is presented in detail. A comparative study on techniques, datasets, and performance metrics used in state-of-the-art literature in BC diagnosis is also summarized. The proposed work provides a review of recent advancements in DL techniques for enhancing BC diagnosis.

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Section: Inference From the Reviewmentioning

confidence: 99%

“…Based on this feature fusion concept, deep features derived from different breast imaging modalities could be a better choice for the future research direction. 44,45…”

Section: Inference From the Reviewmentioning

confidence: 99%