Implementation of DenseNet121 Based on Convolutional Neural1 Network with Geometric Augmentation for Breast Cancer2 Histopathology Image Classification

Abstract

The development of a robust deep learning architecture that is not easily affected by overfitting is an important factor in improving the performance of medical image classification systems. This study aims to assess the ability of the DenseNet121 architecture to classify histopathological images into two categories. The model utilizes pre-trained weights from ImageNet and is adjusted through fine-tuning, while geometric data augmentation techniques are performed to increase sample variation. The training process utilizes the AdamW optimizer and the Binary Cross-Entropy loss function, with performance assessment using binary classification metrics. The test results show that DenseNet121 achieved a training accuracy of 98.96%, a validation accuracy of 97.72%, and a testing accuracy of 97.09%, indicating consistent performance at each stage and no signs of overfitting. This finding indicates that DenseNet121 has great potential as an effective structure in histopathological image classification systems.