A Dual-Selective Channel Attention Network for Osteoporosis Prediction in Computed Tomography Images of Lumbar Spine

Liu, Xue; Hou, Yuchuan; Wang, Shiwei; Luo, Cheng; Xia, Zihao; Geng, Qingshan; Liu, Shuang; Wang, Zhongliang; Gao, Wenshan; Yang, Kun

doi:10.56578/ataiml010105

Cited by 7 publications

(4 citation statements)

References 30 publications

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“…This is carried out to ensure that the image processing procedure is only located in the area detected as shrimp. The minimum limit of shrimp objects or the image is cropped with a square ROI and saved with a different file name [43]. The extraction process is searching for unique feature information in the images, such as color, texture, and shape [44].…”

Section: Methodsmentioning

confidence: 99%

Shrimp Body Weight Estimation in Aquaculture Ponds Using Morphometric Features Based on Underwater Image Analysis and Machine Learning Approach

Setiawan¹,

Hadiyanto²,

Widodo³

2022

RIA

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Shrimp is a marine culture found globally due to the ability of its yields to boost a country's economy. It is imperative to monitor its size to determine the condition of the shrimp underwater with complex noise using a non-invasive method. Therefore, this study aims to develop a new method for measuring the body weight of shrimp using morphometric features based on underwater image analysis and a machine learning approach. The method used consists of several steps, data collection using an underwater camera, image analysis using image grayscale, image binary, edge detection, region of interest detection, shrimp image morphometric features extraction, camera calibration using Triangle Similarity (TS), and Correction Factor (CF), calculation of morphometric features value, create machine learning model, training data, and testing data for estimation of underwater shrimp body weight. After testing the model, get the best accuracy value is RMSE = 0.05, MAE = 0.04, and R2 = 0.96 from the MLR method. In conclusion, the results showed that the hybrid method TS-CF-MLR is the best method for measuring underwater shrimp body weight estimation with the lowest error rate and highest coefficient of determination.

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Section: Methodsmentioning

confidence: 99%

Shrimp Body Weight Estimation in Aquaculture Ponds Using Morphometric Features Based on Underwater Image Analysis and Machine Learning Approach

Setiawan¹,

Hadiyanto²,

Widodo³

2022

RIA

View full text Add to dashboard Cite

show abstract

“…In the latest study, Xue et al conducted a study in which they labeled the L1–L4 vertebral body in CT images and divided it into three categories based on bone mineral density: osteoporosis, osteopenia, and normal. The study achieved a high level of accuracy, with a prediction accuracy of 83.4% and a recall rate of 90.0% [ 31 ]. Dzierżak and Omiotek have developed a novel method for diagnosing osteoporosis through the use of spine CT imaging and deep convolutional neural networks.…”

Section: Related Workmentioning

confidence: 99%

End to End Multitask Joint Learning Model for Osteoporosis Classification in CT Images

Zhang

Lin

Pan

et al. 2023

Computational Intelligence and Neuroscience

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Osteoporosis is a significant global health concern that can be difficult to detect early due to a lack of symptoms. At present, the examination of osteoporosis depends mainly on methods containing dual-energy X-ray, quantitative CT, etc., which are high costs in terms of equipment and human time. Therefore, a more efficient and economical method is urgently needed for diagnosing osteoporosis. With the development of deep learning, automatic diagnosis models for various diseases have been proposed. However, the establishment of these models generally requires images with only lesion areas, and annotating the lesion areas is time-consuming. To address this challenge, we propose a joint learning framework for osteoporosis diagnosis that combines localization, segmentation, and classification to enhance diagnostic accuracy. Our method includes a boundary heat map regression branch for thinning segmentation and a gated convolution module for adjusting context features in the classification module. We also integrate segmentation and classification features and propose a feature fusion module to adjust the weight of different levels of vertebrae. We trained our model on a self-built dataset and achieved an overall accuracy rate of 93.3% for the three label categories (normal, osteopenia, and osteoporosis) in the testing datasets. The area under the curve for the normal category is 0.973; for the osteopenia category, it is 0.965; and for the osteoporosis category, it is 0.985. Our method provides a promising alternative for the diagnosis of osteoporosis at present.

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“…-Other techniques exist such as those based on support vector machines, neural networks, methods adapted to high dimensions by subspace construction or dimension reduction [18][19][20].…”

Section: Literaturementioning

confidence: 99%

Faulty Detection System Based on SPC and Machine Learning Techniques

Benrabah¹,

Kadri²,

Mouss³

et al. 2022

RIA

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Starting from a worrying observation, that companies have difficulties controlling the anomalies of their manufacturing processes, in order to have a better control over them, we have realized a case study on the practical data of the Fertial Complex to analyze the main parameters of the ammonia neutralization by nitric acid process. This article proposes a precise diagnostic of this process to detect dysfunction problems affecting the final product. We start with a general diagnosis of the process using the SPC method, this approach is considered an excellent way to monitor and improve the product quality and provides very useful observations that allowed us to detect the parameters that suffer from problems affecting the quality. After the discovery of the parameters incapable to produce the quality required by the standards, we applies two machine learning technologies dedicated to the type of data of these parameters for detected the anomaly, the first technique called The kernel connectivity-based outlier factor (COF) algorithm consists in recording for each object the degree of being an outlier, the second technique called the Isolation Forest, its principle is to establish a forest to facilitate the calculation and description. The results obtained were compared in order to choose which is the best algorithm to monitor and detect the problems of these parameters, we find that the COF method is more efficient than the isolation forest which leads us to rely on this technology in this kind of process in order to avoid passing a bad quality to the customer in future.

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A Dual-Selective Channel Attention Network for Osteoporosis Prediction in Computed Tomography Images of Lumbar Spine

Cited by 7 publications

References 30 publications

Shrimp Body Weight Estimation in Aquaculture Ponds Using Morphometric Features Based on Underwater Image Analysis and Machine Learning Approach

Shrimp Body Weight Estimation in Aquaculture Ponds Using Morphometric Features Based on Underwater Image Analysis and Machine Learning Approach

End to End Multitask Joint Learning Model for Osteoporosis Classification in CT Images

Faulty Detection System Based on SPC and Machine Learning Techniques

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