Breast cancer diagnosis: A systematic review

Wen, Xin; Guo, Xing; Wang, Shuihua; Lu, Zhihai; Zhang, Yudong

doi:10.1016/j.bbe.2024.01.002

Cited by 1 publication

(1 citation statement)

References 141 publications

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“…These modern approaches leverage the ability of deep learning models to automatically learn feature representations from mammograms, bypassing the need for manual feature extraction. This shift towards deep learning has revolutionized the Computer-Aided Diagnosis (CAD) system for breast cancer detection, enabling the development of models that are not only more accurate but also capable of handling the complex variations found in breast tissues and tumor appearances [30]. In the domain of BCD, the literature describes deep learning-based techniques for the analysis of mammogram images.…”

Section: Current State-of-the-art In Bcdmentioning

confidence: 99%

Breast Cancer Diagnosis Using YOLO-Based Multiscale Parallel CNN and Flattened Threshold Swish

Mohammed,

Ekmekci

2024

Applied Sciences

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In the field of biomedical imaging, the use of Convolutional Neural Networks (CNNs) has achieved impressive success. Additionally, the detection and pathological classification of breast masses creates significant challenges. Traditional mammogram screening, conducted by healthcare professionals, is often exhausting, costly, and prone to errors. To address these issues, this research proposes an end-to-end Computer-Aided Diagnosis (CAD) system utilizing the ‘You Only Look Once’ (YOLO) architecture. The proposed framework begins by enhancing digital mammograms using the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique. Then, features are extracted using the proposed CNN, leveraging multiscale parallel feature extraction capabilities while incorporating DenseNet and InceptionNet architectures. To combat the ‘dead neuron’ problem, the CNN architecture utilizes the ‘Flatten Threshold Swish’ (FTS) activation function. Additionally, the YOLO loss function has been enhanced to effectively handle lesion scale variation in mammograms. The proposed framework was thoroughly tested on two publicly available benchmarks: INbreast and CBIS-DDSM. It achieved an accuracy of 98.72% for breast cancer classification on the INbreast dataset and a mean Average Precision (mAP) of 91.15% for breast cancer detection on the CBIS-DDSM. The proposed CNN architecture utilized only 11.33 million parameters for training. These results highlight the proposed framework’s ability to revolutionize vision-based breast cancer diagnosis.

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Section: Current State-of-the-art In Bcdmentioning

confidence: 99%