Multimodal Imaging of Target Detection Algorithm under Artificial Intelligence in the Diagnosis of Early Breast Cancer

Abstract

This study aimed to analyze the diagnostic value of multimodal images based on artificial intelligence target detection algorithms for early breast cancer, so as to provide help for clinical imaging examinations of breast cancer. This article combined residual block with inception block, constructed a new target detection algorithm to detect breast lumps, used deep convolutional neural network and ultrasound imaging in diagnosing benign and malignant breast lumps, took breast density grading with mammography, compared the convolutional neural network (CNN) algorithm with the proposed algorithm, and then applied the proposed algorithm to the diagnosis of 120 female patients with breast lumps. According to the results, accuracy rates of breast lump detection (94.76%), benign and malignant breast lumps diagnosis (98.22%), and breast grading (93.65%) with the algorithm applied in this study were significantly higher than those (75.67%, 87.23%, and 79.54%) with CNN algorithm, and the difference was statistically significant ( P < 0.05); among 62 patients with malignant breast lumps of the 120 patients with breast lumps, 37 were patients with invasive ductal carcinoma, 8 with lobular carcinoma in situ, 16 with intraductal carcinoma, and 4 with mucinous carcinoma; among the remaining 58 patients with benign breast lumps, 28 were patients with fibrocystic breast disease, 17 with intraductal papilloma, 4 with breast hyperplasia, and 9 with adenopathy; the differences in shape, growth direction, edge, and internal echo of multimodal ultrasound imaging of patients with benign and malignant breast lumps had statistical significance ( P < 0.05); the malignant constituent ratios of patients with breast density grades I to IV were 0%, 7.10%, 80.40%, and 100%, respectively. In short, the multimodal imaging diagnosis under the algorithm in this article was superior to CNN algorithm in all aspects; according to the judgment on benign and malignant breast lumps and breast density with multimodal imaging features, the higher the breast density, the higher the probability of breast cancer.

Bioinformatics Method Was Used to Analyze the Highly Expressed Gene FAM83A of Breast Cancer in Young Women

“…It can be speculated that the gene can not only play a role in regulating signal transduction but also be adjusted by other adjustment proteins and has a number of adjusted functional sites, such as phosphorylation sites. ELM predicts that it has a PLCXc domain of phosphoyositol-specific phospholipase C, with MotifScan [ 48 ]. Predicted results with phospholipase D functional domains are similar.…”

Convolutional neural network based on automatic segmentation of peritumoral shear-wave elastography images for predicting breast cancer

“…Recently, deep convolutional neural network (CNN)-based approaches have been considered as an effective approach for the feature extraction and classification of US images in breast cancer diagnosis (15)(16)(17)(18). However, most of the CNN models used in the diagnosis of breast cancer have been based on the US or SWE images of intratumoral tissue rather than peritumoral tissue (15)(16)(17)(18)(19)(20). The peritumoral stiffness of breast lesions is an accurate predictor of breast cancer (9)(10)(11)(12).…”

Radiogenomics, Breast Cancer Diagnosis and Characterization: Current Status and Future Directions

“…Early detection is a key step for BC diagnosis in order to improve survival. In the last decades, imaging emerged as a powerful tool both for early detection and the characterization of BC as well as for the subsequent monitoring of therapy response (i.e., [ 3 , 4 ]). Technological advances in image technologies as well as in imaging processing approaches have also contributed to give a central role to different diagnostic imaging tools such as X-ray mammography, ultrasound, magnetic resonance imaging (MRI) and positron emission tomography (PET) for BC diagnosis (e.g., [ 5 , 6 , 7 , 8 , 9 ]).…”