Detection and Classification of Breast Lesions with You Only Look Once Version 5

Meng, Mingzhu; Zhang, Ming; Shen, Dong; He, Guangyuan; Guo, Yesong

doi:10.2217/fon-2022-0593

Cited by 4 publications

(5 citation statements)

References 39 publications

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“…The proposed YOLO-BCD framework demonstrates the highest average precision scores using the FTS activation function for both Mass (90.95%) and Calcification (Cal) (91.35%) detection, leading to an impressive mean Average Precision of 91.15%. This surpasses other models, such as the Faster RCNN by Ibrokhimov et al [33], with an mAP of 79.30%, the YOLO V3-based model by Zhang et al [20], with an mAP of 78.76%, YOLO V5 by Meng et al [21], with an mAP of 83.84%, and YOLO-LOGO by Su et al [22], with an mAP of 88.60%. These results collectively underscore the superior accuracy of the YOLO-BCD framework in detecting breast cancer indicators.…”

Section: Breast Cancer Detection Comparative Analysismentioning

confidence: 67%

“…Meanwhile, the YOLO V3-based model proposed by Zhang et al [20] marked an improvement, yielding a precision of 90.94%, a recall of 89.83%, an F1-Score of 90.36%, and an accuracy of 92.16%. Further advancements were observed in the YOLO V5-based framework by Meng et al [21], which accomplished a precision of 92.08%, a recall of 91.31%, an F1-Score of 91.67%, and an accuracy of 93.72%. The YOLO-LOGO model by Su et al [22] achieved 95.53% precision, 95.47% recall, 95.50% F1-Score, and 96.51% accuracy.…”

Section: Breast Cancer Classification Comparative Analysismentioning

confidence: 99%

“…Meng et al [21] focused on evaluating the You Only Look Once version 5 (YOLOv5) for detecting and classifying breast lesions in dynamic contrast-enhanced MRI (DCE-MRI). Four YOLOv5 sub-models were tested on a dataset comprising over 2000 images, each of benign and malignant lesions.…”

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

confidence: 99%

“…As the disease progresses, the lesions occupy a larger area, leading to variations in lesion size at different stages, even if the lesion type remains the same. The 'You Only Look Once' (YOLO) detection technique, a single-stage detection method, has been widely used for BCD in recent years [5,11,[19][20][21][22]. However, its loss function is not scale-invariant and is biased towards smaller lesions [23].…”

Section: Introductionmentioning

confidence: 99%

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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: Breast Cancer Detection Comparative Analysismentioning

confidence: 67%

Section: Breast Cancer Classification Comparative Analysismentioning

confidence: 99%

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

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Mohammed,

Ekmekci

2024

Applied Sciences

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“…The region of interest was cropped using You Only Look Once version 5 (YOLOv5) to focus on the cervical spine. 19 One hundred images were prelabeled by an orthopedic surgeon for YOLOv5 model training. The cropped images were resized to a unified size of 512×512 pixels.…”

Section: Methodsmentioning

confidence: 99%

Detection of Cervical Foraminal Stenosis from Oblique Radiograph Using Convolutional Neural Network Algorithm

Kim,

Yang,

Song

et al. 2024

Yonsei Med J

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Purpose This study was conducted to develop a convolutional neural network (CNN) algorithm that can diagnose cervical foraminal stenosis using oblique radiographs and evaluate its accuracy. Materials and Methods A total of 997 patients who underwent cervical MRI and cervical oblique radiographs within a 3-month interval were included. Oblique radiographs were labeled as “foraminal stenosis” or “no foraminal stenosis” according to whether foraminal stenosis was present in the C2–T1 levels based on MRI evaluation as ground truth. The CNN model involved data augmentation, image preprocessing, and transfer learning using DenseNet161. Visualization of the location of the CNN model was performed using gradient-weight class activation mapping (Grad-CAM). Results The area under the curve (AUC) of the receiver operating characteristic curve based on DenseNet161 was 0.889 (95% confidence interval, 0.851–0.927). The F1 score, accuracy, precision, and recall were 88.5%, 84.6%, 88.1%, and 88.5%, respectively. The accuracy of the proposed CNN model was significantly higher than that of two orthopedic surgeons (64.0%, p <0.001; 58.0%, p <0.001). Grad-CAM analysis demonstrated that the CNN model most frequently focused on the foramen location for the determination of foraminal stenosis, although disc space was also frequently taken into consideration. Conclusion A CNN algorithm that can detect neural foraminal stenosis in cervical oblique radiographs was developed. The AUC, F1 score, and accuracy were 0.889, 88.5%, and 84.6%, respectively. With the current CNN model, cervical oblique radiography could be a more effective screening tool for neural foraminal stenosis.

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Using deep learning models in magnetic resonance cholangiopancreatography images to diagnose common bile duct stones

Luo,

Li,

Zhang

et al. 2023

Scandinavian Journal of Gastroenterology

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Detection and Classification of Breast Lesions with You Only Look Once Version 5

Cited by 4 publications

References 39 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

Detection of Cervical Foraminal Stenosis from Oblique Radiograph Using Convolutional Neural Network Algorithm

Using deep learning models in magnetic resonance cholangiopancreatography images to diagnose common bile duct stones

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