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Machined brass skin collimation with variable thickness for electron therapy
Title
Machined brass skin collimation with variable thickness for electron therapy.
Creator
Gomez, Facenda Alianna, Ouhib, Zoubir, Florida Atlantic University, Department of Physics, Charles E. Schmidt College of Science
Abstract/Description
Skin collimation in electron therapy ensures sharper penumbra and maximal protection to adjacent critical structures. It also provides a better clinical dose to the target and avoids recurrences at the periphery. The thickness of the electron skin collimation must be adequate for shielding purposes, not too thick to cause discomfort to the patient and be conformal to the skin. This study assessed the clinical potential of machined brass skin collimation with variable thickness. Brass...
Show moreSkin collimation in electron therapy ensures sharper penumbra and maximal protection to adjacent critical structures. It also provides a better clinical dose to the target and avoids recurrences at the periphery. The thickness of the electron skin collimation must be adequate for shielding purposes, not too thick to cause discomfort to the patient and be conformal to the skin. This study assessed the clinical potential of machined brass skin collimation with variable thickness. Brass transmission factors for 6, 9, and 12 MeV electron beams were measured and used to determine the skin collimation clinically acceptable thickness. Dosimetric performance of the variable thickness skin collimation was evaluated for 9 MeV electrons within a rectilinear water-equivalent phantom and a water-filled head phantom. Results showed the variable thickness skin collimation is dosimetrically equivalent to the uniform thickness collimation. Favorable dosimetric advantages for brass skin collimation for small electron fields were achieved.
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Date Issued
2021
PURL
http://purl.flvc.org/fau/fd/FA00013731
Subject Headings
Skin, Radiotherapy, Electron beams
Format
Document (PDF)
Cloud-based Skin Lesion Diagnosis System using Convolutional Neural Networks
Title
Cloud-based Skin Lesion Diagnosis System using Convolutional Neural Networks.
Creator
Akar, Esad, Furht, Borko, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Skin cancer is a major medical problem. If not detected early enough, skin cancer like melanoma can turn fatal. As a result, early detection of skin cancer, like other types of cancer, is key for survival. In recent times, deep learning methods have been explored to create improved skin lesion diagnosis tools. In some cases, the accuracy of these methods has reached dermatologist level of accuracy. For this thesis, a full-fledged cloud-based diagnosis system powered by convolutional neural...
Show moreSkin cancer is a major medical problem. If not detected early enough, skin cancer like melanoma can turn fatal. As a result, early detection of skin cancer, like other types of cancer, is key for survival. In recent times, deep learning methods have been explored to create improved skin lesion diagnosis tools. In some cases, the accuracy of these methods has reached dermatologist level of accuracy. For this thesis, a full-fledged cloud-based diagnosis system powered by convolutional neural networks (CNNs) with near dermatologist level accuracy has been designed and implemented in part to increase early detection of skin cancer. A large range of client devices can connect to the system to upload digital lesion images and request diagnosis results from the diagnosis pipeline. The diagnosis is handled by a two-stage CNN pipeline hosted on a server where a preliminary CNN performs quality check on user requests, and a diagnosis CNN that outputs lesion predictions.
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Date Issued
2018
PURL
http://purl.flvc.org/fau/fd/FA00013150
Subject Headings
Skin Diseases--diagnosis, Skin--Cancer--Diagnosis, Diagnosis--Methodology, Neural networks, Cloud computing
Format
Document (PDF)
Experimental Investigation of Skin Friction Drag Reduction on a Flat Plate using Microbubbles
Title
Experimental Investigation of Skin Friction Drag Reduction on a Flat Plate using Microbubbles.
Creator
Grabe, Zachary A., Dhanak, Manhar R., Florida Atlantic University
Abstract/Description
A microbubble generation system has been designed, constructed, and tested in a circulating water tunnel. A 1.0 m long flat plate was subjected to a flow where the Reynolds number ranged from ReL = 7.23x 10^5 - 1.04 x 10^6. Bubble diameters and skin friction measurements were studied at various airflow rates and water velocities. Bubbles were produced by forcing air through porous plates that were mounted flush with the bottom of the test plate. Once emitted through the plates, the bubbles...
Show moreA microbubble generation system has been designed, constructed, and tested in a circulating water tunnel. A 1.0 m long flat plate was subjected to a flow where the Reynolds number ranged from ReL = 7.23x 10^5 - 1.04 x 10^6. Bubble diameters and skin friction measurements were studied at various airflow rates and water velocities. Bubbles were produced by forcing air through porous plates that were mounted flush with the bottom of the test plate. Once emitted through the plates, the bubbles traveled downstream in the boundary layer. The airflow rate and water velocity were found to have the most significant impact on the size of the bubbles created. Skin friction drag measurements were recorded in detail in the velocity and airflow rate ranges. The coefficient of skin friction was determined and relationships were then established between this coefficient and the void ratio.
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Date Issued
2007
PURL
http://purl.flvc.org/fau/fd/FA00012523
Subject Headings
Frictional resistance (Hydrodynamics), Drag (Aerodynamics), Skin friction (Aerodynamics), Fluid mechanics
Format
Document (PDF)
Computer-aided diagnosis of skin cancers using dermatology images
Title
Computer-aided diagnosis of skin cancers using dermatology images.
Creator
Gilani, Syed Qasim, Marques, Oge, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description
Skin cancer is a prevalent cancer that significantly contributes to global mortality rates. Early detection is crucial for a high survival rate. Dermatologists primarily rely on visual inspection to diagnose skin cancers, but this method is inaccurate. Deep learning algorithms can enhance the diagnostic accuracy of skin cancers. However, these algorithms require substantial labeled data for effective training. Acquiring annotated data for skin cancer classification is time-consuming,...
Show moreSkin cancer is a prevalent cancer that significantly contributes to global mortality rates. Early detection is crucial for a high survival rate. Dermatologists primarily rely on visual inspection to diagnose skin cancers, but this method is inaccurate. Deep learning algorithms can enhance the diagnostic accuracy of skin cancers. However, these algorithms require substantial labeled data for effective training. Acquiring annotated data for skin cancer classification is time-consuming, expensive, and necessitates expert annotation. Moreover, skin cancer datasets often suffer from imbalanced data distribution. Generative Adversarial Networks (GANs) can be used to overcome the challenges of data scarcity and lack of labels by automatically generating skin cancer images. However, training and testing data from different distributions can introduce domain shift and bias, impacting the model’s performance. This dissertation addresses this issue by developing deep learning-based domain adaptation models. Additionally, this research emphasizes deploying deep learning models on hardware to enable real-time skin cancer detection, facilitating accurate diagnoses by dermatologists. Deploying conventional deep learning algorithms on hardware is not preferred due to the problem of high resource consumption. Therefore, this dissertation presents spiking neural network-based (SNN) models designed specifically for hardware implementation. SNNs are preferred for their power-efficient behavior and suitability for hardware deployment.
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Date Issued
2023
PURL
http://purl.flvc.org/fau/fd/FA00014233
Subject Headings
Deep learning (Machine learning), Diagnostic imaging, Skin--Cancer--Diagnosis
Format
Document (PDF)
Manufacturing of 3D Printed Boluses for Use In Electron Radiation Therapy
Title
Manufacturing of 3D Printed Boluses for Use In Electron Radiation Therapy.
Creator
Gibbard, Grant, Kalantzis, Georgios, Florida Atlantic University, Charles E. Schmidt College of Science, Department of Physics
Abstract/Description
This research demonstrates that a 3D printed bolus can be customized for electron radiation therapy. Both extruder and powder based printers were used, along with, paraffin wax, super stuff, and H20. The plan dose coverage and conformity for the planning target volume (PTV), was such that the distal side of the PTV was covered by the 90% isodose line. The structure is read, and converted into an STL file. The file is sent to a slicer to print. The object was filled with parafin wax,...
Show moreThis research demonstrates that a 3D printed bolus can be customized for electron radiation therapy. Both extruder and powder based printers were used, along with, paraffin wax, super stuff, and H20. The plan dose coverage and conformity for the planning target volume (PTV), was such that the distal side of the PTV was covered by the 90% isodose line. The structure is read, and converted into an STL file. The file is sent to a slicer to print. The object was filled with parafin wax, superstuff or water and sealed. Materials Hounsfield units were analyzed, along with the structure stability. This method is evaluated by scanning the 3D printed bolus. The dose conformity is improved compared to that with no bolus. By generating a patient specific 3D printed bolus there is an in improvement in conformity of the prescription isodose surface while sparing immediately adjacent normal tissues.
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Date Issued
2017
PURL
http://purl.flvc.org/fau/fd/FA00005943
Subject Headings
Dissertations, Academic -- Florida Atlantic University, Radiotherapy Dosage., Skin--Cancer., Radiotherapy--methods
Format
Document (PDF)
Skin lesion segmentation and classification using deep learning
Title
Skin lesion segmentation and classification using deep learning.
Creator
Burdick, John B., Marques, Oge, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Melanoma, a severe and life-threatening skin cancer, is commonly misdiagnosed or left undiagnosed. Advances in artificial intelligence, particularly deep learning, have enabled the design and implementation of intelligent solutions to skin lesion detection and classification from visible light images, which are capable of performing early and accurate diagnosis of melanoma and other types of skin diseases. This work presents solutions to the problems of skin lesion segmentation and...
Show moreMelanoma, a severe and life-threatening skin cancer, is commonly misdiagnosed or left undiagnosed. Advances in artificial intelligence, particularly deep learning, have enabled the design and implementation of intelligent solutions to skin lesion detection and classification from visible light images, which are capable of performing early and accurate diagnosis of melanoma and other types of skin diseases. This work presents solutions to the problems of skin lesion segmentation and classification. The proposed classification approach leverages convolutional neural networks and transfer learning. Additionally, the impact of segmentation (i.e., isolating the lesion from the rest of the image) on the performance of the classifier is investigated, leading to the conclusion that there is an optimal region between “dermatologist segmented” and “not segmented” that produces best results, suggesting that the context around a lesion is helpful as the model is trained and built. Generative adversarial networks, in the context of extending limited datasets by creating synthetic samples of skin lesions, are also explored. The robustness and security of skin lesion classifiers using convolutional neural networks are examined and stress-tested by implementing adversarial examples.
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Date Issued
2018
PURL
http://purl.flvc.org/fau/fd/FA00013021
Subject Headings
Melanoma, Medical imaging, Deep learning, Skin diseases--Classification, Image segmentation
Format
Document (PDF)
Embodied Mimicry: Lightening Black Bodies in the Visual Rhetoric of Popular 20th Century Black Media
Title
Embodied Mimicry: Lightening Black Bodies in the Visual Rhetoric of Popular 20th Century Black Media.
Creator
Judge-Hemans, Janéa, Heidt, Stephen, Florida Atlantic University, Dorothy F. Schmidt College of Arts and Letters, School of Communication and Multimedia Studies
Abstract/Description
This study analyzes various forms of visual and textual rhetoric found in popular black-owned print media from 1900-1970, including: beauty product advertisements, magazine cover photography and feature articles in order to contribute to a rhetorical history of color bias within the African-American community. The imagery included here validated and encouraged the transformation and lightening of African-American bodies through what I call embodied mimicry in order to achieve dominance within...
Show moreThis study analyzes various forms of visual and textual rhetoric found in popular black-owned print media from 1900-1970, including: beauty product advertisements, magazine cover photography and feature articles in order to contribute to a rhetorical history of color bias within the African-American community. The imagery included here validated and encouraged the transformation and lightening of African-American bodies through what I call embodied mimicry in order to achieve dominance within the racial group and a semblance of acceptance outside of it. Mimicry of white societal standards by African-Americans including: formatting of print media, circulation of beauty ads and physical embodiment of white physical features ultimately re-inscribed the tenets of racism into the black public sphere in the form of colorism. The intention of this research is to analyze the rhetorical history of colorism in order to better understand the current state of colorism in American society.
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Date Issued
2016
PURL
http://purl.flvc.org/fau/fd/FA00004719, http://purl.flvc.org/fau/fd/FA00004719
Subject Headings
African Americans -- Color -- Social aspects, Black race -- Color, Colorism -- United States, Ethnicity in mass media, Human skin color -- Social aspects, Mass media and minorities, Race awareness, Racism in mass media
Format
Document (PDF)