Ibrahim Ahmad, Mohamed Hoda K, Maher Ali, Zhang Baochang
Department of Computer Science, October 6 University, Cairo, Egypt.
Department of Computer Engineering, Ain Shams University, Cairo, Egypt.
Front Artif Intell. 2022 Jun 27;5:884749. doi: 10.3389/frai.2022.884749. eCollection 2022.
In recent years, we have witnessed the fast growth of deep learning, which involves deep neural networks, and the development of the computing capability of computer devices following the advance of graphics processing units (GPUs). Deep learning can prototypically and successfully categorize histopathological images, which involves imaging classification. Various research teams apply deep learning to medical diagnoses, especially cancer diseases. Convolutional neural networks (CNNs) detect the conventional visual features of disease diagnoses, e.g., lung, skin, brain, prostate, and breast cancer. A CNN has a procedure for perfectly investigating medicinal science images. This study assesses the main deep learning concepts relevant to medicinal image investigation and surveys several charities in the field. In addition, it covers the main categories of imaging procedures in medication. The survey comprises the usage of deep learning for object detection, classification, and human cancer categorization. In addition, the most popular cancer types have also been introduced. This article discusses the Vision-Based Deep Learning System among the dissimilar sorts of data mining techniques and networks. It then introduces the most extensively used DL network category, which is convolutional neural networks (CNNs) and investigates how CNN architectures have evolved. Starting with Alex Net and progressing with the Google and VGG networks, finally, a discussion of the revealed challenges and trends for upcoming research is held.
近年来，我们见证了深度学习的快速发展，深度学习涉及深度神经网络，并且随着图形处理单元（GPU）的发展，计算机设备的计算能力也不断提升。深度学习能够对组织病理学图像进行原型化并成功分类，这涉及图像分类。各个研究团队将深度学习应用于医学诊断，尤其是癌症疾病的诊断。卷积神经网络（CNN）可检测疾病诊断中的传统视觉特征，例如肺癌、皮肤癌、脑癌、前列腺癌和乳腺癌。CNN有一套完善的流程来研究医学图像。本研究评估了与医学图像研究相关的主要深度学习概念，并对该领域的几个研究实例进行了调研。此外，它还涵盖了医学成像程序的主要类别。该调研包括深度学习在目标检测、分类以及人类癌症分类方面的应用。此外，还介绍了最常见的癌症类型。本文在不同类型的数据挖掘技术和网络中讨论了基于视觉的深度学习系统。接着介绍了使用最广泛的深度学习网络类别，即卷积神经网络（CNN），并研究了CNN架构是如何演变的。从Alex Net开始，再到谷歌网络和VGG网络，最后对研究中发现的挑战以及未来研究的趋势进行了讨论。
