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 more productive.

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 highlighted.

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.

背景：深度学习 (DL) 是一种人工神经网络驱动的框架，具有多层次的表示，其中非线性模块组合在一起，可以将表示级别从较低的级别增强到非常抽象的级别。尽管深度学习在几乎每个领域都有广泛的应用，但它在疾病诊断和临床试验中的应用极大地为生物科学带来了突破。DL 可以与机器学习结合使用，但有时两者也可以单独使用。DL 似乎是比机器学习更好的平台，因为前者不需要中间特征提取并且适用于更大的数据集。DL 是当今科学家和研究人员中讨论最多的领域之一，用于诊断和解决各种生物学问题。然而，

目标：审查用于疾病诊断的可用 DL 模型和数据集。

方法：对可用的 DL 模型及其在疾病诊断中的应用进行了回顾讨论并制成表格。重点介绍了数据集的类型和一些流行的 DL 疾病相关数据源。

结果：我们分析了常用的 DL 方法、数据类型，并讨论了一些最近用于解决不同生物学问题的深度学习模型。

结论：该综述提供了关于 DL 方法、数据类型和用于疾病诊断的 DL 模型选择的有用见解。