Medical Decision Support System of Common Carotid Artery Using Image Mining and Dual Snake Segmentation Procedure

Kumar, L. Ashok; Rajendran, P.

doi:10.7897/2230-8407.08578

Cited by 3 publications

(2 citation statements)

References 8 publications

(8 reference statements)

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“…Feature extraction is the process of establishing a set of necessary features, or image characteristics, that form the core element and, when expressed in an efficient or comprehensible manner, provide the necessary information for analysis and segmentation [30]. A total of 42 feature extractors were generated for training an RF_Segm model.…”

Section: Feature Extractionmentioning

confidence: 99%

A Random Forest Classifier for Anomaly Detection in Laser-Powder Bed Fusion Using Optical Monitoring

Khan,

Birkhofer,

Kunz

et al. 2023

Materials

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Metal additive manufacturing (AM) is a disruptive production technology, widely adopted in innovative industries that revolutionizes design and manufacturing. The interest in quality control of AM systems has grown substantially over the last decade, driven by AM’s appeal for intricate, high-value, and low-volume production components. Geometry-dependent process conditions in AM yield unique challenges, especially regarding quality assurance. This study contributes to the development of machine learning models to enhance in-process monitoring and control technology, which is a critical step in cost reduction in metal AM. As the part is built layer upon layer, the features of each layer have an influence on the quality of the final part. Layer-wise in-process sensing can be used to retrieve condition-related features and help detect defects caused by improper process conditions. In this work, layer-wise monitoring using optical tomography (OT) imaging was employed as a data source, and a machine-learning (ML) technique was utilized to detect anomalies that can lead to defects. The major defects analyzed in this experiment were gas pores and lack of fusion defects. The Random Forest Classifier ML algorithm is employed to segment anomalies from optical images, which are then validated by correlating them with defects from computerized tomography (CT) data. Further, 3D mapping of defects from CT data onto the OT dataset is carried out using the affine transformation technique. The developed anomaly detection model’s performance is evaluated using several metrics such as confusion matrix, dice coefficient, accuracy, precision, recall, and intersection-over-union (IOU). The k-fold cross-validation technique was utilized to ensure robustness and generalization of the model’s performance. The best detection accuracy of the developed anomaly detection model is 99.98%. Around 79.40% of defects from CT data correlated with the anomalies detected from the OT data.

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Section: Feature Extractionmentioning

confidence: 99%

A Random Forest Classifier for Anomaly Detection in Laser-Powder Bed Fusion Using Optical Monitoring

Khan,

Birkhofer,

Kunz

et al. 2023

Materials

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“…Nagasubramanian et al [53] developed a 3D deep convolutional neural network (DCNN) with eight 3D convolutional layers to extract the deep spectral-spatial features to represent the inoculated stem images from the soybean crops. Kumar et al [54] proposed a 3D convolutional neural network (CNN) with six 3D convolutional layers to extract the spectral-spatial features for various crop diseases.…”

Section: Related Work In Crop Disease Detection Based On Hyperspectra...mentioning

confidence: 99%

Novel CropdocNet Model for Automated Potato Late Blight Disease Detection from Unmanned Aerial Vehicle-Based Hyperspectral Imagery

et al. 2022

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The accurate and automated diagnosis of potato late blight disease, one of the most destructive potato diseases, is critical for precision agricultural control and management. Recent advances in remote sensing and deep learning offer the opportunity to address this challenge. This study proposes a novel end-to-end deep learning model (CropdocNet) for accurate and automated late blight disease diagnosis from UAV-based hyperspectral imagery. The proposed method considers the potential disease-specific reflectance radiation variance caused by the canopy’s structural diversity and introduces multiple capsule layers to model the part-to-whole relationship between spectral–spatial features and the target classes to represent the rotation invariance of the target classes in the feature space. We evaluate the proposed method with real UAV-based HSI data under controlled and natural field conditions. The effectiveness of the hierarchical features is quantitatively assessed and compared with the existing representative machine learning/deep learning methods on both testing and independent datasets. The experimental results show that the proposed model significantly improves accuracy when considering the hierarchical structure of spectral–spatial features, with average accuracies of 98.09% for the testing dataset and 95.75% for the independent dataset, respectively.

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Nanosuspension as a promising drug delivery approach for the antidiabetic drug: An inclusive review on technology and future aspects

Sharma,

Bose,

Moudgil

et al. 2023

The Fourth Scientific Conference for Electrical Engineering Techniques Research (Eetr2022)

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Medical Decision Support System of Common Carotid Artery Using Image Mining and Dual Snake Segmentation Procedure

Cited by 3 publications

References 8 publications

A Random Forest Classifier for Anomaly Detection in Laser-Powder Bed Fusion Using Optical Monitoring

A Random Forest Classifier for Anomaly Detection in Laser-Powder Bed Fusion Using Optical Monitoring

Novel CropdocNet Model for Automated Potato Late Blight Disease Detection from Unmanned Aerial Vehicle-Based Hyperspectral Imagery

Nanosuspension as a promising drug delivery approach for the antidiabetic drug: An inclusive review on technology and future aspects

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