AUTHOR=Ramamoorthy Prabhu , Ramakantha Reddy Buchi Reddy , Askar S. S. , Abouhawwash Mohamed 

TITLE=Histopathology-based breast cancer prediction using deep learning methods for healthcare applications

JOURNAL=Frontiers in Oncology

VOLUME=Volume 14 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2024.1300997

DOI=10.3389/fonc.2024.1300997

ISSN=2234-943X

ABSTRACT=Breast cancer (BC) has been first in female cancer mortality and is a type of cancer that represents a threat to women's health. Deep learning methods have been used extensively in many medical domains recently, especially in detection and classification applications. Studying histological images for the automatic diagnosis of BC is important for patients and their prognosis. Due to the complication and variety of histology images, manual examination needs extensive experience from pathologists can be difficult and susceptible to errors. Therefore, publicly accessible datasets called BreakHis and Invasive Ductal Carcinoma (IDC) are used in this study to analyze histopathological images of BC. Next, using Super-Resolution Generative Adversarial Networks (SRGAN), which creates high-resolution images from low-quality images, the gathered images from BreakHis and IDC are pre-processed to provide useful results in the prediction stage. The components of conventional Generative Adversarial Network (GAN) loss functions and effective sub-pixel nets were combined to create the concept of SRGAN. After that, the high-quality images are sent to the data augmentation stage, where new data points are created by making small adjustments to the dataset using Rotation, Random Cropping, Mirroring, and Color-Shifting. Next, using Inception V3 and Resnet-50, Patch-based Feature Extraction (PFE-INC-RES) is employed to extract the features from the augmentation. After the features have been extracted, the next step involves processing them and applying Transductive Long Short-Term Memory (TLSTM) to improve classification accuracy by decreasing the number of false positives. The results of suggested PFE-INC-RES is evaluated using existing methods on the BreakHis dataset, with respect to accuracy (99.84%), specificity (99.71%), sensitivity (99.78%), and F1-score (99.80%). While the  suggested PFE-INC-RES performed better in IDC dataset based on F1-score (99.08%), accuracy (99.79%), specificity (98.97%), and sensitivity (99.17%), respectively.