A Deep Learning framework and its Implementation for Diabetic Foot Ulcer Classification

Prabhu, Madhava; Verma, Seema

doi:10.1109/icrito51393.2021.9596380

Cited by 7 publications

(1 citation statement)

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“…An interesting and beneficial application of computer vision is its use in the diagnosis of medical conditions and in assisting individuals with disabilities. Many of the sources presented in this review made use of RGB and thermal imaging of patients to perform object detection and image classification to find any signs of medical conditions such as melanoma [ 110 ] or diabetes [ 111 ], while others presented systems for assisting the visually impaired [ 112 ]. In both presented fields of application, while a very high accuracy is of extreme importance, a high inference speed is also paramount to any aides to special needs individuals.…”

Section: Application Based System Comparisonmentioning

confidence: 99%

A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme

Biglari

Tang

2023

Sensors

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Machine learning is an expanding field with an ever-increasing role in everyday life, with its utility in the industrial, agricultural, and medical sectors being undeniable. Recently, this utility has come in the form of machine learning implementation on embedded system devices. While there have been steady advances in the performance, memory, and power consumption of embedded devices, most machine learning algorithms still have a very high power consumption and computational demand, making the implementation of embedded machine learning somewhat difficult. However, different devices can be implemented for different applications based on their overall processing power and performance. This paper presents an overview of several different implementations of machine learning on embedded systems divided by their specific device, application, specific machine learning algorithm, and sensors. We will mainly focus on NVIDIA Jetson and Raspberry Pi devices with a few different less utilized embedded computers, as well as which of these devices were more commonly used for specific applications in different fields. We will also briefly analyze the specific ML models most commonly implemented on the devices and the specific sensors that were used to gather input from the field. All of the papers included in this review were selected using Google Scholar and published papers in the IEEExplore database. The selection criterion for these papers was the usage of embedded computing systems in either a theoretical study or practical implementation of machine learning models. The papers needed to have provided either one or, preferably, all of the following results in their studies—the overall accuracy of the models on the system, the overall power consumption of the embedded machine learning system, and the inference time of their models on the embedded system. Embedded machine learning is experiencing an explosion in both scale and scope, both due to advances in system performance and machine learning models, as well as greater affordability and accessibility of both. Improvements are noted in quality, power usage, and effectiveness.

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Section: Application Based System Comparisonmentioning

confidence: 99%