A Survey of Medical Imaging, Storage and Transfer Techniques

Aparna, R.; Shanmugavadivu, P.

doi:10.1007/978-3-030-00665-5_3

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…(Reiner et al 2005) As CNN increases its applicability and importance in the medical imaging domain, radiology workflows that enable AI models to access medical data are critically important. (Reiner et al 2005, Meenatchi Aparna & Shanmugavadivu 2018 Our design avoids the limitation of usability in the real world by adapting to radiology workflow and adhering to industry norms and standards. Although we did not evaluate the added value of the CADe to the diagnostic performance of radiologists, given the results of previous studies in which radiologists' diagnostic performance improved with deep learning algorithms, we believe that this algorithm can improve radiologists' performance even in largescale population screening.…”

Section: Out-of-distribution Detectionmentioning

confidence: 99%

Towards Clinical Practice: Design and Implementation of Convolutional Neural Network-Based Assistive Diagnosis System for COVID-19 Case Detection from Chest X-Ray Images

Kvak¹,

Bendik²,

Chromcova³

2022

Preprint

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One of the critical tools for early detection and subsequent evaluation of the incidence of lung diseases is chest radiography. This study presents a real-world implementation of a convolutional neural network (CNN) based Carebot Covid app to detect COVID-19 from chest X-ray (CXR) images. Our proposed model takes the form of a simple and intuitive application. Used CNN can be deployed as a STOW-RS prediction endpoint for direct implementation into DICOM viewers. The results of this study show that the deep learning model based on DenseNet and ResNet architecture can detect SARS-CoV-2 from CXR images with precision of 0.981, recall of 0.962 and AP of 0.993.

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Section: Out-of-distribution Detectionmentioning

confidence: 99%

Towards Clinical Practice: Design and Implementation of Convolutional Neural Network-Based Assistive Diagnosis System for COVID-19 Case Detection from Chest X-Ray Images

Kvak¹,

Bendik²,

Chromcova³

2022

Preprint

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Brain Tumor Segmentation in 3D-MRI Based on Artificial Bee Colony and Level Set

Ibrahim

Darwish

Sheta

2020

Advances in Intelligent Systems and Computing

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3D-MRI Brain Tumor Detection Model Using Modified Version of Level Set Segmentation Based on Dragonfly Algorithm

et al. 2020

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Accurate brain tumor segmentation from 3D Magnetic Resonance Imaging (3D-MRI) is an important method for obtaining information required for diagnosis and disease therapy planning. Variation in the brain tumor’s size, structure, and form is one of the main challenges in tumor segmentation, and selecting the initial contour plays a significant role in reducing the segmentation error and the number of iterations in the level set method. To overcome this issue, this paper suggests a two-step dragonfly algorithm (DA) clustering technique to extract initial contour points accurately. The brain is extracted from the head in the preprocessing step, then tumor edges are extracted using the two-step DA, and these extracted edges are used as an initial contour for the MRI sequence. Lastly, the tumor region is extracted from all volume slices using a level set segmentation method. The results of applying the proposed technique on 3D-MRI images from the multimodal brain tumor segmentation challenge (BRATS) 2017 dataset show that the proposed method for brain tumor segmentation is comparable to the state-of-the-art methods.

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A Survey of Medical Imaging, Storage and Transfer Techniques

Cited by 3 publications

References 22 publications

Towards Clinical Practice: Design and Implementation of Convolutional Neural Network-Based Assistive Diagnosis System for COVID-19 Case Detection from Chest X-Ray Images

Towards Clinical Practice: Design and Implementation of Convolutional Neural Network-Based Assistive Diagnosis System for COVID-19 Case Detection from Chest X-Ray Images

Brain Tumor Segmentation in 3D-MRI Based on Artificial Bee Colony and Level Set

3D-MRI Brain Tumor Detection Model Using Modified Version of Level Set Segmentation Based on Dragonfly Algorithm

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