Artificial Intelligence Applications for Osteoporosis Classification Using Computed Tomography

Ong, Wilson; Liu, Ren Wei; Makmur, Andrew; Low, Xi Zhen; Sng, Weizhong Jonathan; Tan, Jiong Hao; Kumar, Naresh; Hallinan, James Thomas Patrick Decourcy

doi:10.3390/bioengineering10121364

Cited by 10 publications

(1 citation statement)

References 187 publications

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“…In recent years, deep learning has demonstrated powerful visual feature learning abilities [10][11][12][13][14]. A large number of deep learning methods are applied in object detection [15], semantic segmentation [16], image classification [17], recommender systems [18] and medical image analysis [19]. However, the application of deep learning methods for downstream tasks in sintering industrial scenarios have some challenges.…”

Section: Introductionmentioning

confidence: 99%

From Segmentation to Classification: A Deep Learning Scheme for Sintered Surface Images Processing

Yang,

Chen,

Zhao

2023

Processes

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Effectively managing the quality of iron ore is critical to iron and steel metallurgy. Although quality inspection is crucial, the perspective of sintered surface identification remains largely unexplored. To bridge this gap, we propose a deep learning scheme for mining the necessary information in sintered images processing to replace manual labor and realize intelligent inspection, consisting of segmentation and classification. Specifically, we first employ a DeepLabv3+ semantic segmentation algorithm to extract the effective material surface features. Unlike the original model, which includes a high number of computational parameters, we use SqueezeNet as the backbone to improve model efficiency. Based on the initial annotation of the processed images, the sintered surface dataset is constructed. Then, considering the scarcity of labeled data, a semi-supervised deep learning scheme for sintered surface classification is developed, which is based on pseudo-labels. Experiments show that the improved semantic segmentation model can effectively segment the sintered surface, achieving 98.01% segmentation accuracy with only a 5.71 MB size. In addition, the effectiveness of the adopted semi-supervised learning classification method based on pseudo-labels is validated in six state-of-the-art models. Among them, the ResNet-101 model has the best classification performance, with 94.73% accuracy for the semi-supervised strategy while only using 30% labeled data, which is an improvement of 1.66% compared with the fully supervised strategy.

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