With time, AI technologies have matured well and resonated in various domains of applied sciences and engineering. The sub-domains of AI, machine learning (ML), deep learning (DL), and associated statistical tools are getting more attention. Therefore, various machine learning models are being created to take advantage of the data available and accomplish tasks, such as automatic prediction, classification, clustering, segmentation and anomaly detection, etc. Tasks like classification need labeled data used to train the models to achieve a reliable accuracy. This study shows the systematic review of promising research areas and applications of DL models in medical diagnosis and medical healthcare systems. The prevalent DL models, their architectures, and related pros, cons are discussed to clarify their prospects. Many deep learning networks have been useful in the field of medical image processing for prognosis and diagnosis of life-threatening ailments (e.g., breast cancer, lung cancer, and brain tumor, etc.), which stand as an error-prone and tedious task for doctors and specialists when performed manually. Medical images are processed using these DL methods to solve various tasks like prediction, segmentation, and classification with accuracy bypassing human abilities. However, the current DL models have some limitations that encourage the researchers to seek further improvement.

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医学科学中深度学习模型的最新技术及其挑战

随着时间的推移，人工智能技术已经成熟并在应用科学和工程的各个领域产生共鸣。人工智能、机器学习 (ML)、深度学习 (DL) 和相关统计工具的子领域越来越受到关注。因此，正在创建各种机器学习模型以利用可用数据并完成任务，例如自动预测、分类、聚类、分割和异常检测等。 分类等任务需要用于训练模型的标记数据以实现可靠的准确性。这项研究显示了对有前途的研究领域和 DL 模型在医疗诊断和医疗保健系统中的应用的系统回顾。讨论了流行的 DL 模型、它们的架构以及相关的优缺点，以阐明它们的前景。许多深度学习网络在医学图像处理领域对危及生命的疾病（例如，乳腺癌、肺癌和脑肿瘤等）的预后和诊断很有用，这是一项容易出错且繁琐的任务为医生和专家手动执行。使用这些 DL 方法处理医学图像以解决各种任务，如预测、分割和分类，准确度超越人类能力。然而，当前的深度学习模型有一些局限性，鼓励研究人员寻求进一步的改进。使用这些 DL 方法处理医学图像以解决各种任务，如预测、分割和分类，准确度超越人类能力。然而，当前的深度学习模型有一些局限性，鼓励研究人员寻求进一步的改进。使用这些 DL 方法处理医学图像以解决各种任务，如预测、分割和分类，准确度超越人类能力。然而，当前的深度学习模型有一些局限性，鼓励研究人员寻求进一步的改进。