Presentation of Novel Architecture for Diagnosis and Identifying Breast Cancer Location Based on Ultrasound Images Using Machine Learning

Pourasad, Yaghoub; Zarouri, Esmaeil; Parizi, Mohammad Salemizadeh; Mohammed, Amin Salih

doi:10.3390/diagnostics11101870

Cited by 30 publications

(14 citation statements)

References 59 publications

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“…The authors achieved accuracy rates lower than 90% with both tasks in ultrasound images collected from clinical practices. Pourasad et al [17] compared the performance of six traditional and deep-learning-based systems for detecting and segmenting tumors in BUS images. In the case of conventional systems, they used the fractal method to select features, and the K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), and Naïve Bayes (NB) classification techniques were used to classify images into normal, benign, and malignant.…”

Section: Related Workmentioning

confidence: 99%

Predicting Breast Tumor Malignancy Using Deep ConvNeXt Radiomics and Quality-Based Score Pooling in Ultrasound Sequences

et al. 2022

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Breast cancer needs to be detected early to reduce mortality rate. Ultrasound imaging (US) could significantly enhance diagnosing cases with dense breasts. Most of the existing computer-aided diagnosis (CAD) systems employ a single ultrasound image for the breast tumor to extract features to classify it as benign or malignant. However, the accuracy of such CAD system is limited due to the large tumor size and shape variation, irregular and ambiguous tumor boundaries, and low signal-to-noise ratio in ultrasound images due to their noisy nature and the significant similarity between normal and abnormal tissues. To handle these issues, we propose a deep-learning-based radiomics method based on breast US sequences in this paper. The proposed approach involves three main components: radiomic features extraction based on a deep learning network, so-called ConvNeXt, a malignancy score pooling mechanism, and visual interpretations. Specifically, we employ the ConvNeXt network, a deep convolutional neural network (CNN) trained using the vision transformer style. We also propose an efficient pooling mechanism to fuse the malignancy scores of each breast US sequence frame based on image-quality statistics. The ablation study and experimental results demonstrate that our method achieves competitive results compared to other CNN-based methods.

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Section: Related Workmentioning

confidence: 99%

Predicting Breast Tumor Malignancy Using Deep ConvNeXt Radiomics and Quality-Based Score Pooling in Ultrasound Sequences

et al. 2022

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“…The authors show that DL tends to perform better than CML. [20] apply a set of machine learning algorithms to analyze ultrasound images to correctly identify the presence of breast cancer. The authors specifically uses k-Nearest Neighbour, Support Vector Machine, Decision Tree, Naïve Bayes to classify data and CNN to classify breast cancer.…”

Section: Literature Reviewmentioning

confidence: 99%

Fuzzy c-Means Clusterization and ANN- MLP Prediction of Malign Breast Cancer in a Cohort of Patients

Massaro¹,

Costantiello²,

Magaletti³

et al. 2022

SSRN Journal

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“…Many imaging tools are available for the prior recognition and early treatment of breast cancer. Breast ultrasound is one of the most commonly used modalities in clinical practice for the diagnosis process [ 6 , 7 ]. Epithelial cells that border the terminal duct lobular unit are the source of the breast cancer.…”

Section: Introductionmentioning

confidence: 99%

Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion

Jabeen

Khan

Alhaisoni

et al. 2022

Sensors

185

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After lung cancer, breast cancer is the second leading cause of death in women. If breast cancer is detected early, mortality rates in women can be reduced. Because manual breast cancer diagnosis takes a long time, an automated system is required for early cancer detection. This paper proposes a new framework for breast cancer classification from ultrasound images that employs deep learning and the fusion of the best selected features. The proposed framework is divided into five major steps: (i) data augmentation is performed to increase the size of the original dataset for better learning of Convolutional Neural Network (CNN) models; (ii) a pre-trained DarkNet-53 model is considered and the output layer is modified based on the augmented dataset classes; (iii) the modified model is trained using transfer learning and features are extracted from the global average pooling layer; (iv) the best features are selected using two improved optimization algorithms known as reformed differential evaluation (RDE) and reformed gray wolf (RGW); and (v) the best selected features are fused using a new probability-based serial approach and classified using machine learning algorithms. The experiment was conducted on an augmented Breast Ultrasound Images (BUSI) dataset, and the best accuracy was 99.1%. When compared with recent techniques, the proposed framework outperforms them.

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Presentation of Novel Architecture for Diagnosis and Identifying Breast Cancer Location Based on Ultrasound Images Using Machine Learning

Cited by 30 publications

References 59 publications

Predicting Breast Tumor Malignancy Using Deep ConvNeXt Radiomics and Quality-Based Score Pooling in Ultrasound Sequences

Predicting Breast Tumor Malignancy Using Deep ConvNeXt Radiomics and Quality-Based Score Pooling in Ultrasound Sequences

Fuzzy c-Means Clusterization and ANN- MLP Prediction of Malign Breast Cancer in a Cohort of Patients

Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion

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