Search results for "ihosyöpä"

Discriminating Basal Cell Carcinoma and Bowen’s Disease with Novel Hyperspectral Imaging System and Convolutional Neural Networks

2022

Discovering knowledge in various applications with a novel hyperspectral imager

2013

Hyperspectral imaging system in the delineation of Ill-defined basal cell carcinomas : a pilot study

2019

Background Basal cell carcinoma (BCC) is the most common skin cancer in the Caucasian population. Eighty per cent of BCCs are located on the head and neck area. Clinically ill‐defined BCCs often represent histologically aggressive subtypes, and they can have subtle subclinical extensions leading to recurrence and the need for re‐excisions. Objectives The aim of this pilot study was to test the feasibility of a hyperspectral imaging system (HIS) in vivo in delineating the preoperatively lateral margins of ill‐defined BCCs on the head and neck area. Methods Ill‐defined BCCs were assessed clinically with a dermatoscope, photographed and imaged with HIS. This was followed by surgical procedures…

Protecting Young Children Against Skin Cancer : Parental Beliefs, Roles, And Regret

2017

Objective To examine the role of parental beliefs, roles, and anticipated regret toward performing childhood sun-protective behaviours. Methods Parents (N = 230; 174 mothers, 56 fathers), recruited using a nonrandom convenience sample, of at least 1 child aged between 2 and 5 years completed an initial questionnaire assessing demographics and past behaviour as well as theory of planned behaviour global (attitude, subjective norm, and perceived behavioural control) and belief-based (behavioural, normative, and control beliefs) measures, role construction, and anticipated regret regarding their intention and behaviour to protect their child from the sun. Two weeks later, participants complete…

Comparison of Machine Learning Methods in Stochastic Skin Optical Model Inversion

2020

In this study, we compare six different machine learning methods in the inversion of a stochastic model for light propagation in layered media, and use the inverse models to estimate four parameters of the skin from the simulated data: melanin concentration, hemoglobin volume fraction, and thicknesses of epidermis and dermis. The aim of this study is to determine the best methods for stochastic model inversion in order to improve current methods in skin related cancer diagnostics and in the future develop a non-invasive way to measure the physical parameters of the skin based partially on the results of the study. Of the compared methods, which are convolutional neural network, multi-layer …

Convolutional neural networks in skin cancer detection using spatial and spectral domain

2019

Skin cancers are world wide deathly health problem, where significant life and cost savings could be achieved if detection of cancer can be done in early phase. Hypespectral imaging is prominent tool for non-invasive screening. In this study we compare how use of both spectral and spatial domain increase classification performance of convolutional neural networks. We compare five different neural network architectures for real patient data. Our models gain same or slightly better positive predictive value as clinicians. Towards more general and reliable model more data is needed and collection of training data should be systematic. peerReviewed