Deep learning applications in breast cancer histopathological imaging: diagnosis, treatment, and prognosis

Deep learning applications in breast cancer histopathological imaging: diagnosis, treatment, and prognosis

Blog Article

Abstract Breast cancer is the most common malignant tumor among Visual Perception Evaluation Of Corten Steel: ŞImal Shopping Mall women worldwide and remains one of the leading causes of death among women.Its incidence and mortality rates are continuously rising.In recent years, with the rapid advancement of deep learning (DL) technology, DL has demonstrated significant potential in breast cancer diagnosis, prognosis evaluation, and treatment response prediction.This paper reviews relevant research progress and applies DL models to image enhancement, segmentation, and classification based on large-scale datasets from TCGA and multiple centers.

We employed foundational models such as ResNet50, Transformer, and Hover-net to investigate the performance of DL models in breast cancer diagnosis, treatment, and prognosis prediction.The results indicate that DL techniques have significantly An Improved Convolutional Neural Network (CNN) for Disease Detection and Diagnosis for Multi-crop Plants improved diagnostic accuracy and efficiency, particularly in predicting breast cancer metastasis and clinical prognosis.Furthermore, the study emphasizes the crucial role of robust databases in developing highly generalizable models.Future research will focus on addressing challenges related to data management, model interpretability, and regulatory compliance, ultimately aiming to provide more precise clinical treatment and prognostic evaluation programs for breast cancer patients.