A Novel Deep Learning Architecture for Testis Histology Image Classification

TL;DR

This paper introduces a new deep learning architecture with a hyperlayer for improved testis histology image classification, achieving over 98% accuracy by capturing multi-scale features.

Contribution

The novel architecture incorporates a hyperlayer in stacked auto-encoders to better capture nuanced features in histology images, outperforming traditional networks.

Findings

Achieved over 98% classification accuracy.

Improved feature representation over traditional deep networks.

Effective in distinguishing similar tubule textures.

Abstract

Unlike other histology analysis, classification of tubule status in testis histology is very challenging due to their high similarity of texture and shape. Traditional deep learning networks have difficulties to capture nuance details among different tubule categories. In this paper, we propose a novel deep learning architecture for feature learning, image classification, and image reconstruction. It is based on stacked auto-encoders with an additional layer, called a hyperlayer, which is created to capture features of an image at different layers in the network. This addition effectively combines features at different scales and thus provides a more complete profile for further classification. Evaluation is performed on a set of 10,542 tubule image patches. We demonstrate our approach with two experiments on two different subsets of the dataset. The results show that the features learned from our architecture achieve more than 98% accuracy and represent an improvement over traditional deep network architectures.