Enhancing Histopathological Image Classification via Integrated HOG and Deep Features with Robust Noise Performance

TL;DR

This study improves histopathological image classification by combining HOG and deep features, demonstrating high accuracy and robustness, especially under noisy conditions, using the LC25000 dataset.

Contribution

It introduces an integrated feature approach combining HOG and deep features with robustness analysis in noisy environments for histopathological image classification.

Findings

InceptionResNet-v2 achieved 96.01% accuracy and 96.8% AUC.

Deep features outperformed raw network features, with AUC of 99.99%.

Models using deep features were more noise-robust, especially GBM and KNN.

Abstract

The era of digital pathology has advanced histopathological examinations, making automated image analysis essential in clinical practice. This study evaluates the classification performance of machine learning and deep learning models on the LC25000 dataset, which includes five classes of histopathological images. We used the fine-tuned InceptionResNet-v2 network both as a classifier and for feature extraction. Our results show that the fine-tuned InceptionResNet-v2 achieved a classification accuracy of 96.01\% and an average AUC of 96.8\%. Models trained on deep features from InceptionResNet-v2 outperformed those using only the pre-trained network, with the Neural Network model achieving an AUC of 99.99\% and accuracy of 99.84\%. Evaluating model robustness under varying SNR conditions revealed that models using deep features exhibited greater resilience, particularly GBM and KNN. The combination of HOG and deep features showed enhanced performance, however, less so in noisy environments.