Medical Imaging
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The concept of tuberculosis detection paves a major role in this recent world because, according to the Global Tuberculosis (TB) Report in 2019, more than one million cases are reported per year in India. Even though various tests are available, the chest X-ray is the most important one, without which the detection will be incomplete. In ancient poster anterior chest radiographs, several clinical and diagnostic functions are built by the use of computationally designed algorithms.
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This dataset is in support of my research paper 'Comparison of ElectroMagnetic Emissions & Harmonic Analysis of 20 HP Motor Controlled by 3L NPC Inverter'.
Preprint : https://doi.org/10.36227/techrxiv.19687041.v1
This is useful for manufacturers and r&d engineers for product costing. For more information, results, conclusions on this, pls read research paper.
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The Magnetic Resonance – Computed Tomography (MR-CT) Jordan University Hospital (JUH) dataset has been collected after receiving Institutional Review Board (IRB) approval of the hospital and consent forms have been obtained from all patients. All procedures followed are consistent with the ethics of handling patients’ data.
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The Magnetic Resonance – Computed Tomography (MR-CT) Jordan University Hospital (JUH) dataset has been collected after receiving Institutional Review Board (IRB) approval of the hospital and consent forms have been obtained from all patients. All procedures followed are consistent with the ethics of handling patients’ data.
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Histopathological characterization of colorectal polyps allows to tailor patients' management and follow up with the ultimate aim of avoiding or promptly detecting an invasive carcinoma. Colorectal polyps characterization relies on the histological analysis of tissue samples to determine the polyps malignancy and dysplasia grade. Deep neural networks achieve outstanding accuracy in medical patterns recognition, however they require large sets of annotated training images.
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This is a supplementary data file, providing the data used to evaluate the performance of our 3D fully convolutional neural network. This network removes reverberation noise from ultrasound channel data. This dataset is simulated ultrasound channel data, simulated in Field II Pro, and has artificial reverberation and thermal noise added. This dataset will be linked to our publication, once it is accepted.
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