Sökning: "computer-aided diagnosis"
Visar resultat 1 - 5 av 30 uppsatser innehållade orden computer-aided diagnosis.
1. Automated Interpretation of Lung Ultrasound for COVID-19 and Tuberculosis diagnosis
Master-uppsats, Lunds universitet/Matematik LTHSammanfattning : BACKGROUND. Early and accurate detection of infectious respiratory diseases like COVID-19 and tuberculosis (TB) plays a crucial role in effective management and the reduction of preventable mortality. LÄS MER
2. Exploring Feature Selection Techniques for Machine Learning-based Melanoma Skin Cancer Classification
Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : One of the most globally common types of cancer is skin cancer, where melanoma is the most deadly form. An important and promising tool for diagnosing diseases such as skin cancer is computer aided diagnostics, a tool which utilizes machine learning to predict and classify cancer. LÄS MER
3. Determining Important Features for Melanoma Classification Through Feature Selection
Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Skin cancer is a common disease and malignant melanoma is the most dangerous form of it. Although dangerous, the survival rate of melanoma patients is high if the diagnosis is made at an early stage. Computer aided diagnostics has been shown to have potential in accurately diagnosing the disease utilizing machine learning. LÄS MER
4. Design, implementation and evaluation of a deep learning prototype to classify non-pigmented malignant skin cancer from dermatoscopic images
Master-uppsats, Lunds universitet/Matematik LTHSammanfattning : The current trends for most fair-skinned populations are that the incidence of melanoma and non-pigmented skin lesions is growing, and this growing trend will continue for the upcoming years. The emergence of deep learning networks and their promising results in solving real-world healthcare problems and improving diagnostic accuracy opens new possibilities. LÄS MER
5. GAN-based Automatic Segmentation of Thoracic Aorta from Non-contrast-Enhanced CT Images
Master-uppsats, KTH/Skolan för kemi, bioteknologi och hälsa (CBH)Sammanfattning : The deep learning-based automatic segmentation methods have developed rapidly in recent years to give a promising performance in the medical image segmentation tasks, which provide clinical medicine with an accurate and fast computer-aided diagnosis method. Generative adversarial networks and their extended frameworks have achieved encouraging results on image-to-image translation problems. LÄS MER