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 12 publications

(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%

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

Mohammed,

Ekmekci

2024

Applied Sciences

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Integrating VGG 19 U‐Net for Breast Thermogram Segmentation and Hybrid Enhancement With Optimized Classifier Selection: A Novel Approach to Breast Cancer Diagnosis

Arul Edwin Raj,

Ahmad,

Ananiah Durai

et al. 2024

Int J Imaging Syst Tech

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Early diagnosis of breast cancer is essential for improving patient survival rates and reducing treatment costs. Despite breast thermogram images having high quality, doctors in developing countries often struggle with early diagnosis due to difficulties in interpreting subtle details. Implementing a Computer‐Aided Diagnosis (CAD) system can assist doctors in accurately analyzing these details. This article presents an innovative approach to breast cancer diagnosis using thermal images. The proposed method enhances the quality and clarity of relevant features while preserving sharp and curved edges through U‐Net‐based segmentation for automatic selection of the ROI, advanced hybrid image enhancement techniques, and a machine learning classifier. Subjective analysis compares the processed images with five conventional enhancement techniques, demonstrating the efficiency of the proposed method. The quantitative analysis further validates the effectiveness of the proposed method against five conventional methods using four quality measures. The proposed method achieves superior performance with PSNR of 15.27 for normal and 14.31 for malignant images, AMBE of 6.594 for normal and 7.46 for malignant images, SSIM of 0.829 for normal and 0.80 for malignant images, and DSSIM of 0.084 for normal and 0.14 for malignant images. The classification phase evaluates four classifiers using 13 features from three categories. The Random Forest (RF) classifier with Discrete Wavelet Transform (DWT) based features initially outperformed other classifier features but had limited performance, with accuracy, sensitivity and specificity of 81.8%, 88.8%, and 91%, respectively. To improve this, three categories of features were normalized and converted into two principal components using Principal Component Analysis (PCA) to train the RF classifier, which then showed superior performance with 97.7% accuracy, 96.5% sensitivity, and 98.2% specificity. The dataset utilized in this article is obtained from the Indira Gandhi Centre for Atomic Research (IGCAR), Kalpakkam, India. The entire proposed model is implemented in a Jupyter notebook.

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Enhancing Diagnostic Precision in Breast Cancer Classification Through EfficientNetB7 Using Advanced Image Augmentation and Interpretation Techniques

Mahesh,

Khan,

Mishra

et al. 2024

Int J Imaging Syst Tech

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The precise classification of breast ultrasound images into benign, malignant, and normal categories represents a critical challenge in medical diagnostics, exacerbated by subtle interclass variations and the variable quality of clinical imaging. State‐of‐the‐art approaches largely capitalize on the advanced capabilities of deep convolutional neural networks (CNNs), with significant emphasis on exploiting architectures like EfficientNet that are pre‐trained on extensive datasets. While these methods demonstrate potential, they frequently suffer from overfitting, reduced resilience to image distortions such as noise and artifacts, and the presence of pronounced class imbalances in training data. To address these issues, this study introduces an optimized framework using the EfficientNetB7 architecture, enhanced by a targeted augmentation strategy. This strategy employs aggressive random rotations, color jittering, and horizontal flipping to specifically bolster the representation of minority classes, thereby improving model robustness and generalizability. Additionally, this approach integrates an adaptive learning rate scheduler and implements strategic early stopping to refine the training process and prevent overfitting. This optimized model demonstrates a substantial improvement in diagnostic accuracy, achieving a 98.29% accuracy rate on a meticulously assembled test dataset. This performance significantly surpasses existing benchmarks in the field, highlighting the model's enhanced ability to navigate the intricacies of breast ultrasound image analysis. The high diagnostic accuracy of this model positions it as an invaluable tool in the early detection and informed management of breast cancer, potentially transforming current paradigms in oncological care.

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Breast cancer diagnosis: A systematic review

Cited by 12 publications

References 141 publications

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

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

Integrating VGG 19 U‐Net for Breast Thermogram Segmentation and Hybrid Enhancement With Optimized Classifier Selection: A Novel Approach to Breast Cancer Diagnosis

Enhancing Diagnostic Precision in Breast Cancer Classification Through EfficientNetB7 Using Advanced Image Augmentation and Interpretation Techniques

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