Hybrid Deep Transfer Network and Rotational Sample Subspace Ensemble Learning for Early Cancer Detection

Accurate histopathology cell image classification plays an important role in early cancer detection and diagnosis. Currently, Convolutional Neural Network is used to assist pathologists for histopathology image classification. In the paper, a Min mice model was applied to evaluate the capability of Convolutional Neural Network features for detecting early-stage carcinogenesis. However, due to the limited histopathology images of the mice model, it may cause overfitting for the classification. Hence, hybrid deep transfer network and rotational sample subspace ensemble learning is proposed for the histopathology image classification. First, deep features are obtained by deep transfer network based on regularized loss functions. Then, the rotational sample subspace sampling is applied to increase the diversity between training sets. Subsequently, subspace projection learning is introduced to achieve dimensionality reduction. Finally, the ensemble learning is used for histopathology image classification. The proposed method was tested on 126 histopathology images of the mouse model. The experimental results demonstrate that the proposed method has achieved a remarkable classification accuracy (99.39%, 99.74%, 100%). It has demonstrated that the proposed approach is promising for early cancer diagnosis.