Skin moles and lesions can be the first signs of severe skin diseases such as cancer. This paper presents the development of an end-user device capable of capturing images, segmentation and diagnosis of moles by using the ABCD rule, which stands for analyzing moles' parameters as: asymmetry, border, color, and diameter. These are the main mole characteristics that doctors look at, each of them having a different factor of importance, and depending on these an accurate diagnosis can be given. For the hardware, we developed a small and compact device that can be manipulated easily by anyone without knowledge of medicine, in which we considered a custom-designed 3D enclosure with two white LEDs to control the light. The device has the role of facilitating analysis of the suspicious moles regularly at home, even if only from an indicative and not from a medical point of view. The developed PC software permits the storage of the images in a local database for easy tracking and analysis in time. The image processing developed for the ABCD rule is incorporated into the PC software and tested extensively on the international PH2 database with skin melanoma images to validate our segmentation and criteria evaluation. Using the developed device, we captured mole images for patients, who also took a medical examination by a specialist using the standard dermatoscope. Therefore, we obtained our own database containing 26 images for which we have also the specialists' diagnosis. The performance evaluation measures obtained using our device are-Accuracy: 0.92, Precision: 1.0, Recall: 0.92, F1-score: 0.96.
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http://dx.doi.org/10.3390/s22031123 | DOI Listing |
J Environ Manage
December 2024
Civil Engineering Department, Engineering Faculty, Firat University, 23119, Elazig, Turkey; SEBIZA Technology Limited Company, Firat Technopark, 23350, Elazig, Turkey.
This study focuses on modelling sustainable concretes' mechanical and environmental properties with interpretable artificial intelligence-based automated rule extraction, management of waste materials, and meeting future prospects. In this context, 24 sustainable concrete series containing fly ash and recycled aggregates were produced. Compressive strength tests were performed on these specimens at 7, 28, and 90 days, and their mechanical properties were evaluated.
View Article and Find Full Text PDFSci Rep
November 2024
FMPR, Mohammed V University in Rabat, Rabat, Morocco.
This research introduces a Computer-Aided Diagnosis-system designed aimed at automated detections & classification of tomato leaf diseases, combining traditional handcrafted features with advanced deep learning techniques. The system's process encompasses preprocessing, feature extraction, feature fusion, and classification. It utilizes enhancement filters and segmentation algorithms to isolate with Regions-of-Interests (ROI) in images tomato leaves.
View Article and Find Full Text PDFJ Clin Aesthet Dermatol
January 2024
Dr. Harrison is with Forefront Dermatology in Englewood, Colorado, and is a Diplomat Fellow of the SDPA; she was with the Doctor of Medical Science Program, AT Still University in Mesa, Arizona at the time of writing.
Commun Psychol
February 2024
Department of Child and Adolescent Psychology and Psychiatry, Erasmus MC University Medical Center Rotterdam-Sophia Children's Hospital, Rotterdam, The Netherlands.
Multivariate machine learning techniques are a promising set of tools for identifying complex brain-behavior associations. However, failure to replicate results from these methods across samples has hampered their clinical relevance. Here we aimed to delineate dimensions of brain functional connectivity that are associated with child psychiatric symptoms in two large and independent cohorts: the Adolescent Brain Cognitive Development (ABCD) Study and the Generation R Study (total n = 6935).
View Article and Find Full Text PDFMedicine (Baltimore)
August 2024
Clinical Pharmacy Department, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.
Researchers in Saudi Arabia conducted this study to determine the level of familiarity that pharmacists and physicians possess with the pregnancy and lactation labeling rules established by the Food and Drug Administration. The present study included a cross-sectional survey conducted among pharmacists and physicians working in Saudi Arabia. The sample size was determined using the Rao sample size calculator.
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