Content of review 1, reviewed on July 21, 2020

Comments on abstract, title, references

The abstract needs some modifications.
There is no need to mention the conference name in the abstract. The accuracy of the proposed model should be mention directly.

The title and abstract are well-chosen and written. The research questions are constructed well. There is a shortage of references.

Comments on introduction/background

The introduction is clear and covers the topic questions. The research questions are constructed well. The related work is adequately reported.

Comments on methodology

The process of the selected subject is clear and precise. The methodology is well described. The methods are valid and reliable.

Comments on data and results

The results and conclusions well stated. Titles, columns, and rows are labelled in an appropriate way. Furthermore, I suggest putting the results in charts and figures.

The authors should add a figure that contains some samples of the dataset without any preprocessing

Comments on discussion and conclusions

There is no conclusion section in the paper

The results are discussed from multiple angles and placed into context without being overinterpreted. The conclusions answered the aims of the study. Limitation of the study must be highlighted.

Source

    © 2020 the Reviewer.

References

    S., V. Y., Zhen, C., Xiaohui, X. 2018. Deep Learning Framework for Multi-class Breast Cancer Histology Image Classification. Lecture Notes in Computer Science, 10882.