Sökning: "cancer skolan"

Visar resultat 1 - 5 av 142 uppsatser innehållade orden cancer skolan.

1. 1. Robustness Analysis of Perfusion Parameter Calculations

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Alicia Palmér; [2024]
   Nyckelord :Perfusion; Medical image analysis; Dynamic Contrast Enhanced Magnetic Resonance Imaging; Tofts model; Functional imaging; Optimization; T1 map; Perfusion; Medicinsk bildanalys; Dynamisk kontrastförstärkt magnetisk resonanstomografibildtagning; Tofts-modell; Funktionell bildbehandling; Optimering; T1 karta;

   Sammanfattning : Cancer is one of the most common causes of death worldwide. When given optimal treatment, however, the risk of severe illness may greatly be reduced. Determining optimal treatment in turn requires evaluation of disease progression and response to potential, previous treatment. LÄS MER

3. 2. Uncertainty Estimation in Radiation Dose Prediction U-Net

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Frida Skarf; [2023]
   Nyckelord :Radiation dose prediction models; U-net; quantile regression; Monte Carlo Dropout; epistemic uncertainty estimation; aleatoric uncertainty estimation; Stråldospredicerande modeller; U-net; kvantilregression; Monte Carlo Dropout; epistemisk osäkerhetsskattning; aletorisk osäkerhetsskattning;

   Sammanfattning : The ability to quantify uncertainties associated with neural network predictions is crucial when they are relied upon in decision-making processes, especially in safety-critical applications like radiation therapy. In this paper, a single-model estimator of both epistemic and aleatoric uncertainties in a regression 3D U-net used for radiation dose prediction is presented. LÄS MER

4. 3. Image-classification for Brain Tumor using Pre-trained Convolutional Neural Network

   Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Bushra Alsabbagh; [2023]
   Nyckelord :Brain tumor; Deep learning; Convolutional Neural Network CNN ; diagnosis; Image classification; pre-trained models; dataset; economic impact.; Cancer; Hjärntumör; Artificiell intelligens AI ; djupinlärning; konvolutionellt neuralt nätverk CNN ; Diagnostik; Bildklassificering; förtränade modeller; dataset.;

   Sammanfattning : Brain tumor is a disease characterized by uncontrolled growth of abnormal cells in the brain. The brain is responsible for regulating the functions of all other organs, hence, any atypical growth of cells in the brain can have severe implications for its functions. LÄS MER

5. 4. The impact of pruning Convolutional Neural Networks when classifying skin cancer

   Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Gustaf Larsson; Marcus Odin; [2023]
   Nyckelord :;

   Sammanfattning : Over the past few years, there have been multiple reports showcasing how Convolutional Neural Networks (CNNs) can be used to classify if skin lesions are cancerous or non-cancerous. However, a limitation of CNNs is the large number of parameters resulting in high computation times. LÄS MER

7. 5. A Comparison of Convolutional Neural Networks used in Melanoma Detection : With transfer learning on the PAD-UFES-20 and ISIC datasets

   Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Abdi Gobena; [2023]
   Nyckelord :Machine learning; Neural networks; Skin cancer; PAD-UFES-20; ISIC; Maskininlärning; Neuronnätverk; Hudcancer; PAD-UFES-20; ISIC;

   Sammanfattning : Skin cancer is one of the most common forms of cancer, of which melanoma is the most lethal. Early detection is critical to long term survival rates. The use of machine learning to detect melanoma shows promising results in detecting malignant forms. LÄS MER