In this work, we propose a multiple QUS parameter based technique that employs ESD and MSS, for binary classification of breast lesions. In order to produce improved ESD estimates, we propose a modified frequency domain technique for ESD estimation of breast tissues from the diffuse component of ...
The classification of the breast imaging-reporting and data system (BI-RADS) based on magnetic resonance imaging (MRI) refers to the classification of the degree of lesions according to the image signs of lesions, which is usually subjecti
摘要: In this research, we exploited the deep learning framework to differentiate the distinctive types of lesions and nodules in breast acquired with ultrasound imaging.关键词:deep learning CADx classification breast cancer DOI: 10.1088/1361-6560/aa82ec 被引量: 14 ...
Methods: This study employs image decomposition to obtain fuzzy enhanced and bilateral filtered images to enrich input information of breast lesions. Fuzzy enhanced, bilateral filtered, and original ultrasound images comprise multifeature data, which are presented as inputs to a pre-trained model to ...
As a result, this technique does not detect the breast cancer in women having dense breast. In the beginning phases of malignancy, radiologist could experience the challenges in identifying the lesions. Hence, there is a need for one more screen test for the confirmatio...
Hence, there is an urgent need to develop structured and intelligent systems to help medical professionals diagnose breast tumors with high accuracy. However, developing such systems is challenging because of the high similarity between benign and malignant lesions, irregular tumor boundaries, and the ...
Diagram of breast cancer pathological image classification using MSMV-PFENet. (a) KREM. (b) PFENet. (c) FDNet. MSI: multi-scale input; PIL: preprocessing input layer; CB: convolution block; AAP: adaptive average pooling; D: dimensions; MV: majority vote. Full size image The proposed MVM...
Breast classification and detection using ultrasound imaging is considered a significant step in computer-aided diagnosis systems. Over the previous decades, researchers have proved the opportunities to automate the initial tumor classification and detection. The shortage of popular datasets of ultrasound ima...
Breast cancer is a common malignancy and a leading cause of cancer-related deaths in women worldwide. Its early diagnosis can significantly reduce the morbidity and mortality rates in women. To this end, histopathological diagnosis is usually followed as
Moreover, we added a fifth class to classify normal tissue in order to analyze which of the CNN best categorizes the lesions. We aim to obtain a classifier to apply it in a CAD system that can help in the early detection of breast cancer, reducing the mortality rate and unnecessary ...