A Survey of Deep Learning Techniques for Medical Diagnosis

Hafiz, Abdul Mueed; Bhat, G. Mohiuddin

doi:10.1007/978-981-13-7166-0_16

Cited by 19 publications

(5 citation statements)

References 56 publications

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“…Machine‐learning and deep‐learning‐based techniques are now extensively utilized in various medical fields 35 including medical imaging 36 and orthopedics 37 . In particular, these methods are used to improve the diagnostic power of MRI images and various MRI‐based techniques such as QSM 38 .…”

Section: Discussionmentioning

confidence: 99%

Multivariate use of MRI biomarkers to classify histologically confirmed necrosis in symptomatic total hip arthroplasty

Sherafati

Bauer

Potter

et al. 2020

Journal Orthopaedic Research

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The failure of total hip arthroplasty (THA) is commonly associated with the necrosis of the periprosthetic tissue. To date, there is no established method to noninvasively quantify the progression of such necrosis. Magnetic resonance imaging (MRI) of soft tissues near implants has undergone a recent renaissance due to the development of multispectral metal‐artifact reduction techniques. Advanced analysis of multispectral MRI has been shown capable of detecting small magnetism effects of metallic debris in periprosthetic tissue. The purpose of this study is to demonstrate the diagnostic utility of these MRI‐based tissue‐magnetism signatures. Together with morphological MRI metrics, such as synovial volume and thickness, these measurements are utilized as biomarkers to noninvasively detect soft‐tissue necrosis in symptomatic THA patients (N=78). All subjects underwent an advanced MRI scan before revision surgery and tissue biopsies utilized for necrosis grading. Statistical analyses demonstrated a weak, but significant positive correlation (P = .04) between MRI magnetism signatures and necrosis scores, while indicating no meaningful association between the latter and serum cobalt and chromium ion levels. Receiver‐operating characteristic (ROC) analyses were then performed based on uni‐ and multivariate logistic regression models utilizing the measured MRI biomarkers as predictors of severe necrosis. The area under the curve of the ROC plots for MRI biomarkers as combined predictors were found to be 0.70 and 0.84 for cross‐validation and precision‐recall tests, respectively.

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Section: Discussionmentioning

confidence: 99%

Multivariate use of MRI biomarkers to classify histologically confirmed necrosis in symptomatic total hip arthroplasty

Sherafati

Bauer

Potter

et al. 2020

Journal Orthopaedic Research

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“…A gap was identified within the literature in relation to the use of TinyML technology in healthcare systems. Although there have been other reviews targeting in one way or another the use of ML/DL algorithms, a number of those reviews focused on the specific application of these algorithms categorizing their use based on: type of medical data used [23], medical application [34], [35], diagnosis from medical images [36], and from the privacy and security point of view [37]. Other reviews targeted the edge implementation of ML/DL algorithms reviewing the different edge hardware [26], [28], [29], [32], [33].…”

Section: B Research Gap and Paper Contributionmentioning

confidence: 99%

Embedded Machine Learning Using Microcontrollers in Wearable and Ambulatory Systems for Health and Care Applications: A Review

Diab

Rodriguez‐Villegas

2022

IEEE Access

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The use of machine learning in medical and assistive applications is receiving significant attention thanks to the unique potential it offers to solve complex healthcare problems for which no other solutions had been found. Particularly promising in this field is the combination of machine learning with novel wearable devices. Machine learning models, however, suffer from being computationally demanding, which typically has resulted on the acquired data having to be transmitted to remote cloud servers for inference. This is not ideal from the system's requirements point of view. Recently, efforts to replace the cloud servers with an alternative inference device closer to the sensing platform, has given rise to a new area of research Tiny Machine Learning (TinyML). In this work, we investigate the different challenges and specifications trade-offs associated to existing hardware options, as well as recently developed software tools, when trying to use microcontroller units (MCUs) as inference devices for health and care applications. The paper also reviews existing wearable systems incorporating MCUs for monitoring, and management, in the context of different health and care intended uses. Overall, this work can be used as a kick-start for embedding machine learning models on MCUs, focusing on healthcare wearables.INDEX TERMS Edge ML, embedded machine learning, healthcare, microcontroller, TinyML, wearable.

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“…However, the cost of such diagnosis is still limited and expensive. Deep learning models [ 4 , 5 , 6 , 7 ] are comparatively efficient in performing the classification process from the images and the data. There has been a demand in the field of healthcare diagnosis in precise identification of the abnormality and classifying the category of the disease from the X-ray, Magnetic Resonance Imaging (MRI), Computer Tomography (CT), Positron Emission Tomography (PET) images, and the signal data like the Electrocardiogram (ECG), Electroencephalogram (EEG), and Electromyography(EMG) [ 8 , 9 , 10 , 11 , 12 , 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Srinivasu

SivaSai

Ijaz

et al. 2021

Sensors

488

169

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Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.

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A Survey of Deep Learning Techniques for Medical Diagnosis

Cited by 19 publications

References 56 publications

Multivariate use of MRI biomarkers to classify histologically confirmed necrosis in symptomatic total hip arthroplasty

Multivariate use of MRI biomarkers to classify histologically confirmed necrosis in symptomatic total hip arthroplasty

Embedded Machine Learning Using Microcontrollers in Wearable and Ambulatory Systems for Health and Care Applications: A Review

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

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