Essays about: "diagnos"

Showing result 6 - 10 of 296 essays containing the word diagnos.

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

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

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

    Abstract : 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. READ MORE

  2. 7. Fault Detection and Diagnosis for Automotive Camera using Unsupervised Learning

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Ziyou Li; [2023]
    Keywords : Unsupervised Learning; Autoencoders; Image Clustering; Fault Detection and Diagnosis; Morphological Operations; Hardware-in-Loop; Advanced DriverAssistance System; Oövervakad inlärning; Autoencoders; Bildklustering; Felfindning och Diagnostik; Morfologiska Operationer; Hardware-in-Loop; Avancerade Förarassistanssystem;

    Abstract : This thesis aims to investigate a fault detection and diagnosis system for automotive cameras using unsupervised learning. 1) Can a front-looking wide-angle camera image dataset be created using Hardware-in-Loop (HIL) simulations? 2) Can an Adversarial Autoencoder (AAE) based unsupervised camera fault detection and diagnosis method be crafted for SPA2 Vehicle Control Unit (VCU) using an image dataset created using Hardware-inLoop? 3) Does using AAE surpass the performance of using Variational Autoencoder (VAE) for the unsupervised automotive camera fault diagnosis model? In the field of camera fault studies, automotive cameras stand out for its complex operational context, particularly in Advanced Driver-Assistance Systems (ADAS) applications. READ MORE

  3. 8. Explainable Machine Learning in Cardiovascular Diagnostics

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Alexander Gutell; Ludvig Skare; [2023]
    Keywords : ;

    Abstract : The major challenges in implementing machine learning models in medical applications stemfrom ethical and accountability concerns, which arise from the lack of insight and understandingof the models' inner workings and reasoning. This opaqueness has resulted in the emergenceof a new subfield of machine learning called Explainability, which aims to develop and deploymethods to gain insight into how input data is weighted and propagated through a machinelearning algorithm. READ MORE

  4. 9. Sensitive Detection of Blood Biomarker Pentraxin 3 – Development and Comparison of Amperometric and Capacitive Biosensors in Flow-Injection Systems

    University essay from Lunds universitet/Bioteknik; Lunds universitet/Bioteknik (master)

    Author : Viktor Johansson; [2023]
    Keywords : Bioanalysis; Pentraxin 3; PTX3; FIA; Flow-injection analysis; Sepis; Electopolymerization; Self-assembling monolayer; Amperometry; Capacitance; Biotechnology; Medicine and Health Sciences; Biology and Life Sciences; Technology and Engineering;

    Abstract : Within clinical diagnostics there is always a need for innovative methods that are rapid, sensitive, and more accurate. In this study two bioanalytical systems have been evaluated for pentraxin 3 (PTX3) detection. READ MORE

  5. 10. The effect of model calibration on noisy label detection

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Max Joel Söderberg; [2023]
    Keywords : Area Under the Margin; Calibration; Image classification; Label smoothing; Noisy labels; Overconfidence; Area under marginalen; Kalibrering; Bildklassificering; Etikettutjämning; Brusiga etiketter; Övertro;

    Abstract : The advances in deep neural networks in recent years have opened up the possibility of using image classification as a valuable tool in various areas, such as medical diagnosis from x-ray images. However, training deep neural networks requires large amounts of annotated data which has to be labelled manually, by a person. READ MORE