Real Time Breast Histology Image Classification with a Mobile Phone

Background Deep learning, specifically convolutional neural network, has made a breakthrough in the complex task of computer image recognition. In this study, depthwise separable convolutional neural network (DS-CNN), MobileNets v1, was used in classifying breast cancer histology images on the computer and then on a mobile/smart phone, in real time. This study propose that DS-CNN can be applied for histological image analysis and its network can be transferred to a commercially available mobile phone for real-time histological image analysis captured through the mobile phone camera. Method This study utilizes the DS-CNN on breast cancer histology images downloaded from publicly available repository: https://rdm.inesctec.pt/dataset/nis-2017-003 . Training set images are augmented by rotation and mirroring the images. DS-CNN is trained to classify breast tissue images between 4 categories: i) normal, ii) benign, iii) carcinoma in-situ, and iv) invasive carcinoma. Finally, the trained DS-CNN is deployed on to a mobile phone to classify the images captured through the mobile phone camera in real-time. The output on the mobile phone screen is the real-time image from the camera and its probability of it being one of the 4 categories (from high to low confidence). Accuracy of DS-CNN is assessed, both on the computer and on mobile phone, by whether its prediction with highest confidence matches the true class in the test dataset. Secondary results of sensitivity and specificities were calculated. Results The trained DS-CNN accuracy on the computer reached as high as 86% in 4 class classification. On the mobile phone, accuracy reached 67% in 4 class classification and 78% in 2 class classification (normal or benign vs. in situ or invasive). Training time took less than 30 min on 1.4 GHz Intel Core i5 dual-core CPU. Latency for evaluation on mobile phone was less than 1 second. Demo video of real time histology image analysis with mobile phone can be found here: https://www.youtube.com/watch?v=qx2CdrSuazg Conclusion DS-CNN is a fast and efficient neural network architecture that can learn to distinguish histological images even with limited sample size and computational power. The architecture can be deployed onto a mobile phone and maintain relatively good accuracy through the phone camera. It is likely that the accuracy can be increased by expanding the dataset and with updated CNN that are optimized for mobile phones.