# A Review of Deep Learning Algorithms and Their Applications in Healthcare
## 2 Related Works
Clinicians have started to utilize image segmentation and object detection for urgent and easily missed cases, such as identifying large-arteria occlusions in the brain using radiological images [19], during which severe brain damage may occur in a short period of time (a few minutes). Moreover, cancer histopathology reading can be supplemented with CNNs trained to detect mitotic cells [20] or tumor regions [21]. CNNs have been used to account for survival probability [22] by discovering the biological features of tissues. Long short-term memory (LSTM) [34] RNNs are being tested in medicine for multiple purposes, ranging from clinical measurements of patients in pediatric intensive care units, a dynamic memory model for predictive medicine based on patient history, and prediction of disease onset from longitudinal laboratory tests, to the de-identification of patients' clinical notes [14]. RNN-based language translation [35] has been used to directly translate speech in one language to text in another. If adapted for electronic health records (EHRs), this technique could translate a patient-provider conversation into transcribed text. Classifying the attributes and status of each medical entity from the conversation while accurately summarizing the dialogue is key. The next generation of automatic speech recognition and information extraction models will likely help clinical voice assistants to accurately record patient visits [35]. Despite showing promise in early human-computer interaction experiments, these techniques have yet to be widely used in medical practice. The brain-computer interface (BCI) allows people and machines to interact by analyzing brain activity. Electroencephalography (EEG) is the most viable and noninvasive method of obtaining such information. It has a low signal-to-noise ratio and low spatial resolution. A new method has been proposed using a combination of blind source separation (BSS) to estimate independent components, continuous wavelet transform (CWT) to represent these signals in 2D, and a convolutional neural network (CNN) for classification. The experimental results of 94.66% are competitive with recent state-of-the-art techniques. Regarding the architecture of the CNN, researchers have found that hyper-parameters such as the size of the kernel and the kernel stride of each convolutional layer significantly influence network performance, whereas the number of convolutions has little impact on the final accuracy [26]. In [27], the authors describe a method for developing an open-architecture controller based on reconfigurable hardware in an open-source framework for servo applications. The servo system is a feedback-control system characterized by the outputs of position, speed, and acceleration. The authors have implemented a genetic algorithm for online self-tuning with an emphasis on both high-quality servo control and vibration reduction during linear motion system positioning. The controller was developed using free tools from graphical user interface to logical implementation. Using this approach, it is also possible to make modifications and add updates easily, which lessens the probability of obsolescence. A graphical user interface developed in Python provides speed profiling, controller auto-tuning, measurement of the main parameters, and monitoring of the vibrations of the servo system. Additionally, a method for auto-tuning of the used PID (proportional-integral-derivative) controller has been developed for efficiently tracking trajectory in the linear movement system. The modules are usable by any FPGA (field programmable gate array) manufacturer. Open-source tools have higher performance, due to their distribution and management of logical resources. Moreover, it is multiplatform and could be run on a Linux, Windows, or Mac OS. The authors also developed a Python GUI for monitoring system variables and vibrations generated by the mechanical system. As well as calculating the trapezoidal velocity profiles, this GUI configures gains through serial communication.