Objective: To check the applicability of machine learning algorithms for the computer-aided diagnosis of confocal laser scanning microscopy (CLSM) views of skin lesions.
Study Design: Features, based on spectral properties of the wavelet transform, are very suitable for the automatic analysis because architectural structures at different scales play an important role in diagnosis of CLSM views. The images are discriminated by several machine learning algorithms, based on Bayes-, tree-, rule-, function (numeric)-, and lazy-classifiers.
Results: The function and lazy classifiers delivered best classification results. However, these algorithms deliver no information about the inference mechanism leading to the classification. The tree classifiers provided better results than the rule classifiers. To obtain more insight into the inference process, and to compare it with the diagnostic guidelines of the dermopathologists, we combined the advantages of tree, numerical, and rule classifiers and choose the classification and regression trees (CART) algorithm, which automatically generates accurate inferring rules. The classification results were relocated to the images by use of the inferring rules as diagnostic aid.
Conclusion: The discriminated elements of the skin lesions images show tissue with features in good accordance with typical diagnostic CLSM features.
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Sensors (Basel)
January 2025
Department of Electrical and Information Engineering, Kiel University, 24143 Kiel, Germany.
Clinical motion analysis plays an important role in the diagnosis and treatment of mobility-limiting diseases. Within this assessment, relative (point-to-point) tracking of extremities could benefit from increased accuracy. Given the limitations of current wearable sensor technology, supplementary spatial data such as distance estimates could provide added value.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK.
A generative adversarial network (GAN) makes it possible to map a data sample from one domain to another one. It has extensively been employed in image-to-image and text-to image translation. We propose an EEG-to-EEG translation model to map the scalp-mounted EEG (scEEG) sensor signals to intracranial EEG (iEEG) sensor signals recorded by foramen ovale sensors inserted into the brain.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Data Science, The Catholic University of Korea, Bucheon 14662, Republic of Korea.
Skin cancer accounts for over 40% of all cancer diagnoses worldwide. However, accurately diagnosing skin cancer remains challenging for dermatologists, as multiple types of skin cancer often appear visually similar. The diagnostic accuracy of dermatologists ranges between 62% and 80%.
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January 2025
Space Robotics Research Group (SpaceR), Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, L-1855 Luxembourg, Luxembourg.
Malaria remains a global health concern, with 249 million cases and 608,000 deaths being reported by the WHO in 2022. Traditional diagnostic methods often struggle with inconsistent stain quality, lighting variations, and limited resources in endemic regions, making manual detection time-intensive and error-prone. This study introduces an automated system for analyzing Romanowsky-stained thick blood smears, focusing on image quality evaluation, leukocyte detection, and malaria parasite classification.
View Article and Find Full Text PDFChildren (Basel)
December 2024
School of Medicine, University of Crete, 71 003 Heraklion, Crete, Greece.
Background: Screening for cardiovascular disease (CVD) and its associated risk factors in childhood facilitates early detection and timely preventive interventions. However, limited data are available regarding screening tools and their diagnostic yield when applied in unselected pediatric populations.
Aims: To evaluate the performance of a CVD screening program, based on history, 12-lead ECG and phonocardiography, applied in primary school children.
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