{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["2022"],"submitter":["Du R"],"pubmed_abstract":["The aim of our study was to establish an artificial intelligence tool for the diagnosis of breast disease base on ultrasound (US) images. A deep learning algorithm Efficient-Det assisted US diagnosis method was developed to determine breast suspicious lesions as benign, malignant, or normal. Totally 1181 US images from 487 patients of our hospital and 694 publicly accessible images were employed for modeling, including 558 benign images, 370 malignant images, and 253 normal tissue images. The actual diagnosis results for the patients were determined by the biopsy or surgery. Efficient-Det was first retrained using an exclusive public breast cancer US dataset with transfer learning techniques. A blind test set consisting of 50 benign, 50 malignant, and 50 normal tissue images was randomly p"],"journal":["Journal of oncology"],"pagination":["7733583"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8957444"],"repository":["biostudies-literature"],"pubmed_title":["Discrimination of Breast Cancer Based on Ultrasound Images and Convolutional Neural Network."],"pmcid":["PMC8957444"],"pubmed_authors":["Shi L","Li Y","Du R","Li T","Chen Y","Fei Z"],"additional_accession":[]},"is_claimable":false,"name":"Discrimination of Breast Cancer Based on Ultrasound Images and Convolutional Neural Network.","description":"The aim of our study was to establish an artificial intelligence tool for the diagnosis of breast disease base on ultrasound (US) images. A deep learning algorithm Efficient-Det assisted US diagnosis method was developed to determine breast suspicious lesions as benign, malignant, or normal. Totally 1181 US images from 487 patients of our hospital and 694 publicly accessible images were employed for modeling, including 558 benign images, 370 malignant images, and 253 normal tissue images. The actual diagnosis results for the patients were determined by the biopsy or surgery. Efficient-Det was first retrained using an exclusive public breast cancer US dataset with transfer learning techniques. A blind test set consisting of 50 benign, 50 malignant, and 50 normal tissue images was randomly p","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022","modification":"2025-04-04T07:44:29.054Z","creation":"2025-04-04T07:44:29.054Z"},"accession":"S-EPMC8957444","cross_references":{"pubmed":["35345516"],"doi":["10.1155/2022/7733583"]}}