Automated Classification of Histopathology Images Using Transfer Learning

TL;DR

This paper demonstrates that transfer learning with DenseNet-161 and ResNet-50 models can accurately classify histopathology images, significantly outperforming existing methods in identifying tissue characteristics across multiple categories.

Contribution

The study introduces the use of pre-trained DenseNet-161 and ResNet-50 models for automated histopathology image classification, achieving high accuracy and outperforming state-of-the-art techniques.

Findings

DenseNet-161 achieved 97.89% accuracy on grayscale images.

ResNet-50 achieved 98.87% accuracy on color images.

Proposed models outperform existing methods across all metrics.

Abstract

There is a strong need for automated systems to improve diagnostic quality and reduce the analysis time in histopathology image processing. Automated detection and classification of pathological tissue characteristics with computer-aided diagnostic systems are a critical step in the early diagnosis and treatment of diseases. Once a pathology image is scanned by a microscope and loaded onto a computer, it can be used for automated detection and classification of diseases. In this study, the DenseNet-161 and ResNet-50 pre-trained CNN models have been used to classify digital histopathology patches into the corresponding whole slide images via transfer learning technique. The proposed pre-trained models were tested on grayscale and color histopathology images. The DenseNet-161 pre-trained model achieved a classification accuracy of 97.89% using grayscale images and the ResNet-50 model obtained the accuracy of 98.87% for color images. The proposed pre-trained models outperform state-of-the-art methods in all performance metrics to classify digital pathology patches into 24 categories.