Current Search: Skin--Cancer. (x)
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- 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
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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.
Show less - 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)
- 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.
Show less - 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)
- 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
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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.
Show less - 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)