Breast Tumor Classification in Digital Tomosynthesis Based on Deep Learning Radiomics
نویسندگان
چکیده
Breast cancer is the most frequently diagnosed in women globally. Early and accurate detection classification of breast tumors are critical improving treatment strategies increasing patient survival rate. Digital tomosynthesis (DBT) an advanced form mammography that aids better early diagnosis disease. This paper proposes a tumor method based on analyzing evaluating performance various innovative deep learning models cooperation with support vector machine (SVM) classifier for DBT dataset. Specifically, we study ability to use transfer from non-medical images classify unseen medical images. In addition, utilize fine-tuning technique improve accuracy.
منابع مشابه
Breast tissue classification in digital tomosynthesis images based on global gradient minimization and texture features
Digital breast tomosynthesis (DBT) is a pseudo-three-dimensional x-ray imaging modality proposed to decrease the effect of tissue superposition present in mammography, potentially resulting in an increase in clinical performance for the detection and diagnosis of breast cancer. Tissue classification in DBT images can be useful in risk assessment, computer-aided detection and radiation dosimetry...
متن کاملDigital Breast Tomosynthesis (DBT)
DBT is a three-dimensional (3D) breast imaging technology that uses a rotating X-ray source to acquire multiple image slices at several angles. The X-ray source rotates around the breast in an arc. Serial exposures are taken every few degrees in the arc rotation. These images are then reconstituted by software to produce a 3Dimage of the breast, similar to CAT scanning. It has been suggested th...
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ژورنال
عنوان ژورنال: Frontiers in artificial intelligence and applications
سال: 2022
ISSN: ['1879-8314', '0922-6389']
DOI: https://doi.org/10.3233/faia220348