Background: Deep learning (DL) is an Artificial neural network-driven framework with
multiple levels of representation for which non-linear modules combined in such a way that the levels
of representation can be enhanced from lower to a much abstract level. Though DL is used widely in
almost every field, it has largely brought a breakthrough in biological sciences as it is used in disease
diagnosis and clinical trials. DL can be clubbed with machine learning, but at times both are used individually
as well. DL seems to be a better platform than machine learning as the former does not require
an intermediate feature extraction and works well with larger datasets. DL is one of the most discussed
fields among scientists and researchers these days for diagnosing and solving various biological problems.
However, deep learning models need some improvisation and experimental validations to be
Objective: To review the available DL models and datasets that are used in disease diagnosis.
Methods: Available DL models and their applications in disease diagnosis were reviewed discussed
and tabulated. Types of datasets and some of the popular disease-related data sources for DL were
Results: We have analyzed the frequently used DL methods, data types, and discussed some of the recent
deep learning models used for solving different biological problems.
Conclusion: The review presents useful insights about DL methods, data types, and selection of DL
models for the disease diagnosis.