A deep learning-based model for defect detection in laser-powder bed fusion using in-situ thermographic monitoring

Baumgartl, Hermann; Tomas, Josef; Buettner, Ricardo; Merkel, Markus

doi:10.1007/s40964-019-00108-3

Cited by 164 publications

(67 citation statements)

References 49 publications

(78 reference statements)

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“…Especially in image processing tasks, Convolutional Neural Networks (CNNs) have established themselves as a successful approach due to their superior performance [18][19][20][21], which is particularly important in the healthcare domain [11][12][13][14][15][16]21]. The automated extraction of descriptive and discriminative features is among the major advantages of CNNs [18,21].…”

Section: Introductionmentioning

confidence: 99%

High-Performance Detection of Corneal Ulceration Using Image Classification with Convolutional Neural Networks

Gross¹,

Breitenbach²,

Baumgartl³

et al. 2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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Corneal Ulcer, also known as keratitis, represents the most frequently appearing symptom among corneal diseases, the second leading cause of ocular morbidity worldwide. Consequences such as irreversible eyesight damage or blindness require an innovative approach that enables a distinction to be made between patterns of different ulcer stages to lower the global burden of visual disability. This paper describes a Convolutional Neural Network-based image classification approach that allows the identification of different types of Corneal Ulcers based on fluorescein staining images. With a balanced accuracy of 92.73 percent, our results set a benchmark in distinguishing between general ulcer patterns. Our proposed method is robust against light reflections and allows automated extraction of meaningful features, manifesting a strong practical and theoretical relevance. By identifying Corneal Ulcers at an early stage, we aid reduction of aggravation by preventively applying and consequently tracking the efficacy of adapted medical treatment, which contributes to IT-based healthcare.

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Section: Introductionmentioning

confidence: 99%

High-Performance Detection of Corneal Ulceration Using Image Classification with Convolutional Neural Networks

Gross¹,

Breitenbach²,

Baumgartl³

et al. 2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…Deep learning (DL) approaches can be used as a late step in most fusion strategies (Lee, Mohammad & Henning, 2018). Most of CT and CXR images in medical applications can be handcrafted and fuzed in score level fusion strategy (Baumgartl et al, 2020).…”

Section: Deep Learning In Image Fusion Strategiesmentioning

confidence: 99%

A novel perceptual two layer image fusion using deep learning for imbalanced COVID-19 dataset

Elzeki

Elfattah²,

Salem

et al. 2021

PeerJ Computer Science

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Background and Purpose COVID-19 is a new strain of viruses that causes life stoppage worldwide. At this time, the new coronavirus COVID-19 is spreading rapidly across the world and poses a threat to people’s health. Experimental medical tests and analysis have shown that the infection of lungs occurs in almost all COVID-19 patients. Although Computed Tomography of the chest is a useful imaging method for diagnosing diseases related to the lung, chest X-ray (CXR) is more widely available, mainly due to its lower price and results. Deep learning (DL), one of the significant popular artificial intelligence techniques, is an effective way to help doctors analyze how a large number of CXR images is crucial to performance. Materials and Methods In this article, we propose a novel perceptual two-layer image fusion using DL to obtain more informative CXR images for a COVID-19 dataset. To assess the proposed algorithm performance, the dataset used for this work includes 87 CXR images acquired from 25 cases, all of which were confirmed with COVID-19. The dataset preprocessing is needed to facilitate the role of convolutional neural networks (CNN). Thus, hybrid decomposition and fusion of Nonsubsampled Contourlet Transform (NSCT) and CNN_VGG19 as feature extractor was used. Results Our experimental results show that imbalanced COVID-19 datasets can be reliably generated by the algorithm established here. Compared to the COVID-19 dataset used, the fuzed images have more features and characteristics. In evaluation performance measures, six metrics are applied, such as QAB/F, QMI, PSNR, SSIM, SF, and STD, to determine the evaluation of various medical image fusion (MIF). In the QMI, PSNR, SSIM, the proposed algorithm NSCT + CNN_VGG19 achieves the greatest and the features characteristics found in the fuzed image is the largest. We can deduce that the proposed fusion algorithm is efficient enough to generate CXR COVID-19 images that are more useful for the examiner to explore patient status. Conclusions A novel image fusion algorithm using DL for an imbalanced COVID-19 dataset is the crucial contribution of this work. Extensive results of the experiment display that the proposed algorithm NSCT + CNN_VGG19 outperforms competitive image fusion algorithms.

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“…To further evaluate the model, we will benchmark our Random Forest algorithm to other ML approaches, including convolutional neural networks [21,[77][78][79] and other ML methods like XGBoost [80] or support vector machines [81] on brain activities in the identified areas for detecting EDS [62] and further research on EEG data Furthermore, we will implement the IT artifact in a clinical environment. Figure 3 shows the related framework used for detection, treatment, and evaluation.…”

Section: Future Workmentioning

confidence: 99%

Machine Learning Based Diagnostics of Developmental Coordination Disorder using Electroencephalographic Data

Buettner

Buechele²,

Grimmeisen³

et al. 2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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We report on promising results concerning the fast and accurate diagnosis of developmental coordination disorder (DCD) which heavily impacts the life of affected children with emotional and behavioral issues. Using a machine learning classifier on spectral data of electroencephalography (EEG) recordings and unfolding the traditional frequency bandwidth in a fine-graded equidistant 99-point spectrum we were able to reach an accuracy of over 99.35 percent having only one misclassification. Our machine learning work contributes to healthcare and information systems research. While current diagnostic methods in use are either complicated, time-consuming, or inaccurate, our automated machine-based approach is accurate and reliable. Our results also provide more insights into the relationship between DCD and brain activity which could stimulate future work in medicine.

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A deep learning-based model for defect detection in laser-powder bed fusion using in-situ thermographic monitoring

Cited by 164 publications

References 49 publications

High-Performance Detection of Corneal Ulceration Using Image Classification with Convolutional Neural Networks

High-Performance Detection of Corneal Ulceration Using Image Classification with Convolutional Neural Networks

A novel perceptual two layer image fusion using deep learning for imbalanced COVID-19 dataset

Machine Learning Based Diagnostics of Developmental Coordination Disorder using Electroencephalographic Data

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