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Subject: search.filters.subject.Osteoporosis

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    Comparison of Transfer Learning Model Accuracy for Osteoporosis Classification on Knee Radiograph
    (IEEE, 2022-02-02) Moussa Mahamat Boukar; Steve Adeshina
    In terms of financial costs and human suffering, osteoporosis poses a serious public health burden. Reduced bone mass, degeneration of the micro architecture of bone tissue, and an increased risk of fracture are its main skeletal symptoms. Osteoporosis is caused not just by low bone mineral density, but also by other factors such as age, weight, height, and lifestyle. Recent advancement in Artificial Intelligence (AI) has led to successful applications of expert systems that use Deep Learning techniques for osteoporosis diagnosis based on some modalities such as dental radiographs amongst others. This study uses a dataset of knee radiographs (i.e., knee-Xray images) to apply and compare three robust transfer learning model algorithms: GoogLeNet, VGG-16, and ResNet50 to classify osteoporosis. From the statistical analysis and scikit learn python analysis, the accuracy of the GoogLeNet model was 90%, the accuracy of the VGG-16 model was 87% and lastly, the accuracy of the ResNet- 50 model was 83%.
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    Transfer Learning Model Training Time Comparison for Osteoporosis Classification on Knee Radiograph of RGB and Grayscale Images
    (WSEAS TRANSACTIONS on ELECTRONICS, 2022-09-13) Moussa Mahamat Boukar; Steve Adeshina; Senol Dane
    In terms of financial costs and human suffering, osteoporosis poses a serious public health burden. Reduced bone mass, degeneration of the microarchitecture of bone tissue, and an increased risk of fracture are its main skeletal symptoms. Osteoporosis is caused not just by low bone mineral density, but also by other factors such as age, weight, height, and lifestyle. Recent advancement in Artificial Intelligence (AI) has led to successful applications of expert systems that use Deep Learning techniques for osteoporosis diagnosis based on some modalities such as dental radiographs amongst others. This study uses a dataset of knee radiographs (i.e., knee-Xray images) to apply and compare the training time of two robust transfer learning model algorithms: GoogLeNet, VGG-16, and ResNet50 to classify osteoporosis. The dataset was split into two subcategories using python opencv library: Grayscale Images and Red Green Blue (RGB) images. From the scikit learn python analysis, the training time of the GoogLeNet model on grayscale images and RGB images was 42minutes and 50 minutes respectively. The VGG-16 model training time on grayscale images and RGB images was 37 minutes and 44 minutes respectively. In addition, to compare the diagnostic performance of the two models, several state-of-the-art neural networks metric was used.
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    Evaluation of Parameter Fine-Tuning with Transfer Learning for Osteoporosis Classification in Knee Radiograph
    ((IJACSA) International Journal of Advanced Computer Science and Applications, 2022-02-02) Moussa Mahamat Boukar; Steve Adeshina
    Osteoporosis is a bone disease that raises the risk of fracture due to the density of the bone mineral being low and the decline of the structure of bone tissue. Among other techniques, such as Dual-Energy X-ray Absorptiometry (DXA), 2D x-ray pictures of the bone can be used to detect osteoporosis. This study aims to evaluate deep convolutional neural networks (CNNs), applied with transfer learning techniques, to categorize specific osteoporosis features in knee radiographs. For objective labeling, we obtained a selection of patient knee x-ray images. The study makes use of the Visual Geometry Group Deep (VGG-16), and VGG-16 with fine-tuning. In this work, the deployed CNNs were assessed using state-of-the-art metrics such as accuracy, sensitivity, and specificity. The evaluation shows that fine-tuning enhanced the VGG-16 CNN's effectiveness for detecting osteoporosis in radiographs of the knee. The accuracy of the VGG-16 with parameter fine-tuning was 88% overall, while the accuracy of the VGG-16 without parameter fine-tuning was 80%.