A Systematic Approach to the Design and Characterization of a Smart Insole for Detecting Vertical Ground Reaction Force (vGRF) in Gait Analysis

Tahir, Anas; Chowdhury, Muhammad E. H.; Khandakar, Amith; Al-Hamouz, Sara; Abdalla, Merna; Awadallah, Sara; Reaz, Mamun Bin Ibne; Al‐Emadi, Nasser

doi:10.3390/s20040957

Cited by 84 publications

(49 citation statements)

References 41 publications

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“…Log loss (Equation (10), also known as logistic loss or cross-entropy loss, was used as the loss function. In Equation (10), N denotes the size of the image set (400) and p denotes the probability that the given image is pneumonia infected. Figure 5 shows the process followed to find the optimal weight corresponding to each model.…”

Section: Weighted Classifiermentioning

confidence: 99%

“…In contrast, X-rays are the most common and widely available diagnostic imaging technique, playing a crucial role in clinical care and epidemiological studies [8,9]. There are several regions across the globe where there is a scarce availability of practiced healthcare workers and radiologists whose prediction on such diseases matter greatly [10][11][12]. Computer-aided diagnosis using artificial intelligence based solutions is becoming increasingly popular these days [13,14].…”

Section: Introductionmentioning

confidence: 99%

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Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning

et al. 2020

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Pneumonia causes the death of around 700,000 children every year and affects 7% of the global population. Chest X-rays are primarily used for the diagnosis of this disease. However, even for a trained radiologist, it is a challenging task to examine chest X-rays. There is a need to improve the diagnosis accuracy. In this work, an efficient model for the detection of pneumonia trained on digital chest X-ray images is proposed, which could aid the radiologists in their decision making process. A novel approach based on a weighted classifier is introduced, which combines the weighted predictions from the state-of-the-art deep learning models such as ResNet18, Xception, InceptionV3, DenseNet121, and MobileNetV3 in an optimal way. This approach is a supervised learning approach in which the network predicts the result based on the quality of the dataset used. Transfer learning is used to fine-tune the deep learning models to obtain higher training and validation accuracy. Partial data augmentation techniques are employed to increase the training dataset in a balanced way. The proposed weighted classifier is able to outperform all the individual models. Finally, the model is evaluated, not only in terms of test accuracy, but also in the AUC score. The final proposed weighted classifier model is able to achieve a test accuracy of 98.43% and an AUC score of 99.76 on the unseen data from the Guangzhou Women and Children’s Medical Center pneumonia dataset. Hence, the proposed model can be used for a quick diagnosis of pneumonia and can aid the radiologists in the diagnosis process.

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Section: Weighted Classifiermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning

et al. 2020

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“…Accordingly, many computer-aided diagnosis (CAD) systems and computer algorithm diagnostic tools have been proposed by researchers for X-ray image analysis; these proposed systems help radiologists discover various types of chest X-ray pneumonia immediately after acquisition. Recently, various biomedical problems, such as skin cancer detection, brain tumor detection, and breast cancer detection are using solutions based on Artificial Intelligence (AI) approaches [6][7][8][9] as handcrafted techniques, deep learning, and machine learning techniques. As a matter of fact, deep learning is a subsection of AI and machine learning that utilizes multi-layered artificial neural networks to provide the latest technology in many topics, such as speech recognition, language translation, and others.…”

Section: Introductionmentioning

confidence: 99%

Deep-Pneumonia Framework Using Deep Learning Models Based on Chest X-Ray Images

2020

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Pneumonia is a contagious disease that causes ulcers of the lungs, and is one of the main reasons for death among children and the elderly in the world. Several deep learning models for detecting pneumonia from chest X-ray images have been proposed. One of the extreme challenges has been to find an appropriate and efficient model that meets all performance metrics. Proposing efficient and powerful deep learning models for detecting and classifying pneumonia is the main purpose of this work. In this paper, four different models are developed by changing the used deep learning method; two pre-trained models, ResNet152V2 and MobileNetV2, a Convolutional Neural Network (CNN), and a Long Short-Term Memory (LSTM). The proposed models are implemented and evaluated using Python and compared with recent similar research. The results demonstrate that our proposed deep learning framework improves accuracy, precision, F1-score, recall, and Area Under the Curve (AUC) by 99.22%, 99.43%, 99.44%, 99.44%, and 99.77%, respectively. As clearly illustrated from the results, the ResNet152V2 model outperforms other recently proposed works. Moreover, the other proposed models—MobileNetV2, CNN, and LSTM-CNN—achieved results with more than 91% in accuracy, recall, F1-score, precision, and AUC, and exceed the recently introduced models in the literature.

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“…Currently many biomedical complications (e.g., brain tumor detection, breast cancer detection, etc.) are using Artificial Intelligence (AI) based solutions [16][17][18][19]. Deep learning techniques can reveal image features, which are not apparent in the original images.…”

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confidence: 99%

Classification of COVID-19 from Chest X-ray images using Deep Convolutional Neural Networks

Asif

Hou

et al. 2020

Preprint

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The COVID-19 pandemic continues to have a devastating effect on the health and well-being of the global population. A vital step in the combat towards COVID-19 is a successful screening of contaminated patients, with one of the key screening approaches being radiological imaging using chest radiography. This study aimed to automatically detect COVID-19 pneumonia patients using digital chest x-ray images while maximizing the accuracy in detection using deep convolutional neural networks (DCNN). The dataset consists of 864 COVID-19, 1345 viral pneumonia and 1341 normal chest x-ray images. In this study, DCNN based model Inception V3 with transfer learning have been proposed for the detection of coronavirus pneumonia infected patients using chest X-ray radiographs and achieved more than 96% accuracy. The results demonstrate that transfer learning proved to be effective, showed robust performance and easily deployable approach for COVID-19 detection.

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A Systematic Approach to the Design and Characterization of a Smart Insole for Detecting Vertical Ground Reaction Force (vGRF) in Gait Analysis

Cited by 84 publications

References 41 publications

Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning

Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning

Deep-Pneumonia Framework Using Deep Learning Models Based on Chest X-Ray Images

Classification of COVID-19 from Chest X-ray images using Deep Convolutional Neural Networks

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