Classification of Giemsa staining chromosome using input-aware deep convolutional neural network with integrated uncertainty estimates

A fully automated chromosome analysis system can substitute cytogenetic experts for the task of chromosome karyotype analysis, which in turn can substantially increase the efficiency of disease diagnosis. However, the construction of such a system is most crucially restricted by the accuracy of chromosome classification, during karyotype analysis. To facilitate the construction of an automatic chromosome analysis system, an input-aware and probabilistic prediction convolutional neural network (IAPP-CNN) is presented in this paper for high accuracy of chromosome classification. The approach follows three stages and consists of one input-aware module, one feature extractor module and one probabilistic prediction module. In the first stage, the input-aware module develops raw images automatically into the global-scale image, the object-scale image and the part-scale image, by introducing an attention mechanism. In the second stage, the three scale images are input into the feature extraction module through three branches, then the respective feature operators are obtained via their independent CNN feature extractors. In the third stage, the probabilistic prediction module uses three dynamic probabilistic parameters to estimate the prediction of each CNN branch separately, and then combined the three CNN votes for the final decision. The feature expression ability of the key feature was improved and the network was enabled to focus on the recognizable regions in the image. Evaluation results from a large dataset of healthy patients showed that the proposed IAPP-CNN achieved the highest accuracy of 99.2% for the chromosome classification task, surpassing the performance of a competitive baseline created by state-of-the-art methods.