Enhanced Multi-Class Breast Cancer Classification from Whole-Slide Histopathology Images Using a Proposed Deep Learning Model

Diagnostics, Journal Year: 2025, Volume and Issue: 15(5), P. 582 - 582

Published: Feb. 27, 2025

Background/Objectives: Breast cancer is among the most frequently diagnosed cancers and leading cause of mortality worldwide. The accurate classification breast from histology photographs very important for diagnosis effective treatment planning. Methods: In this article, we propose a DenseNet121-based deep learning model detection multi-class classification. experiments were performed using whole-slide histopathology images collected BreakHis dataset. Results: proposed method attained state-of-the-art performance with 98.50% accuracy an AUC 0.98 binary classification, it obtained competitive results 92.50% 0.94. Conclusions: outperforms methods in distinguishing between benign malignant tumors as well classifying specific malignancy subtypes. This study highlights potential establishes foundation developing advanced diagnostic tools.

Language: Английский

Ensemble Deep Learning-Based Image Classification for Breast Cancer Subtype and Invasiveness Diagnosis from Whole Slide Image Histopathology

Published: May 10, 2024

Cancer diagnosis and classification are pivotal for effective patient management treatment planning. In this study, we present a comprehensive approach utilizing ensemble deep learning techniques to analyze breast cancer histopathology images. Our datasets based on two widely employed from different centers tasks: BACH BreakHis. Within the Dataset, deployed an strategy incorporating VGG16 ResNet50 architec-tures achieve precise of Introducing novel image patching technique, preprocess high-resolution image, which facilitates focused analysis localized regions interest. The annotated dataset encompasses 400 WSIs across four distinct classes: Normal, Benign, Situ Carcinoma, Invasive Carcinoma. addition, BreakHis dataset, VGG16, ResNet34, models classify mi-croscopic images into eight categories (four benign malignant). For both leveraged five-fold cross-validation rigorous training testing. Preliminary ex-perimental results indicate Patch accuracy 95.31% (on dataset) WSI 98.43% (BreakHis). This research significantly contributes on-going endeavors in harnessing artificial intelligence advance diagnosis, potentially fostering improved outcomes alleviating healthcare burdens.

Language: Английский

Detecting cell types and densities in the tumor microenvironment improves prognostic risk assessment for breast cancer

Biomolecules and Biomedicine, Journal Year: 2024, Volume and Issue: 25(1), P. 106 - 114

Published: Aug. 16, 2024

A comprehensive evaluation of the relationship between densities various cell types in breast cancer tumor microenvironment and patient prognosis is currently lacking. Additionally, absence a large patch-level whole slide imaging (WSI) dataset with annotated hinders ability artificial intelligence to evaluate density WSI. We first employed Lasso-Cox regression build assessment model based on population study. Pathology experts manually containing over 70,000 patches used transfer learning ResNet152 develop an for identifying different these patches. The results showed that significant prognostic differences were observed among patients stratified by score (P = 0.0018), identified as independent factor < 0.05). In validation cohort, predictive performance overall survival (OS) was satisfactory, area under curve (AUC) values 0.893 at 1-year, 0.823 3-year, 0.861 5-year intervals. trained robust ResNet152, achieving 99% classification accuracy These achievements offer new public resources tools personalized treatment assessment.

Language: Английский