ArticleDiagnostics (Basel, Switzerland)2021
Presentation of Novel Architecture for Diagnosis and Identifying Breast Cancer Location Based on Ultrasound Images Using Machine Learning.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- YOLO AI model based on an automated breast volume scanner for the detection of benign and malignant breast lesions.Quantitative imaging in medicine and surgery · 2025Article
- An Automated Decision Support System to Analyze Malignancy Patterns of Breast Masses Employing Medically Relevant Features of Ultrasound Images.Journal of imaging informatics in medicine · 2024Article
- State-of-the-Art of Breast Cancer Diagnosis in Medical Images via Convolutional Neural Networks (CNNs).Journal of healthcare informatics research · 2023Review
- Breast Cancer Diagnosis Based on IoT and Deep Transfer Learning Enabled by Fog Computing.Diagnostics (Basel, Switzerland) · 2023Article
- Public health implications of computer-aided diagnosis and treatment technologies in breast cancer care.AIMS public health · 2023Review
- Radiomics Analysis of Breast Lesions in Combination with Coronal Plane of ABVS and Strain Elastography.Breast cancer (Dove Medical Press) · 2023Article
- Convolutional neural network based on automatic segmentation of peritumoral shear-wave elastography images for predicting breast cancer.Frontiers in oncology · 2023Article
- Predicting Breast Tumor Malignancy Using Deep ConvNeXt Radiomics and Quality-Based Score Pooling in Ultrasound Sequences.Diagnostics (Basel, Switzerland) · 2022Article
- Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion.Sensors (Basel, Switzerland) · 2022Article
- Classification of breast cancer using a manta-ray foraging optimized transfer learning framework.PeerJ. Computer science · 2022Article
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4 authors.
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Abstract
Breast cancer is one of the main causes of death among women worldwide. Early detection of this disease helps reduce the number of premature deaths. This research aims to design a method for identifying and diagnosing breast tumors based on ultrasound images. For this purpose, six techniques have been performed to detect and segment ultrasound images. Features of images are extracted using the fractal method. Moreover, k-nearest neighbor, support vector machine, decision tree, and Naïve Bayes classification techniques are used to classify images. Then, the convolutional neural network (CNN) architecture is designed to classify breast cancer based on ultrasound images directly. The presented model obtains the accuracy of the training set to 99.8%. Regarding the test results, this diagnosis validation is associated with 88.5% sensitivity. Based on the findings of this study, it can be concluded that the proposed high-potential CNN algorithm can be used to diagnose breast cancer from ultrasound images. The second presented CNN model can identify the original location of the tumor. The results show 92% of the images in the high-performance region with an AUC above 0.6. The proposed model can identify the tumor's location and volume by morphological operations as a post-processing algorithm. These findings can also be used to monitor patients and prevent the growth of the infected area.
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