mentioning that the results are just a lower bound of accuracy) to avoid useless improvement, and releasing the code library for complete evaluation procedures. Through these works, we hope to make comparing and testing models fairer and quicker, emphasize the importance of open source codes and the benchmark study in this field, and provide some suggestions and discussions of future studies.

The contributions of this paper are listed as follows:

1)  *Various datasets and data preparing*. We gather most of the publicly available datasets and give the detailed discussion about its adaptability to DL-based intelligent diagnosis. For data preparing, we first discuss different kinds of input formats and different normalization methods for listed datasets. After that, we state that data augmentation which is a common step in CV and NLP might be important to make the training datasets more diverse, and we also try some kinds of data augmentation methods to clarify that they have not been fully investigated. Meanwhile, we also discuss the way of data split and state that it may be more appropriate to split data according to time sequences (also called order split).

2)  *Benchmark accuracy and further studies*. We evaluate various DL-based intelligent diagnosis algorithms 