Comparison of Deep Feature Classification and Fine Tuning for Breast Cancer Histopathology Image Classification

Abstract

Convolutional Neural Networks (ConvNets) are increasingly being used for medical image diagnostic applications. In this paper, we compare two transfer learning approaches - Deep Feature classification and Fine-tuning ConvNets for Diagnosing Breast Cancer malignancy. BreaKHis dataset is used to benchmark our results with ResNet-50, InceptionV2 and DenseNet-169 pre-trained models. Deep feature classification accuracy ranges from 81% to 95% using Logistic Regression, LightGBM and Random Forest classifiers. Fine-tuned DenseNet-169 model accuracy outperformed all other classification models with 99.25 ± 0.4%.