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Ground-Target Recognition Method Based on Transfer Learning.
Sensors (Basel)
January 2025
College of Communication Engineering, Jilin University, Changchun 130012, China.
A moving ground-target recognition system can monitor suspicious activities of pedestrians and vehicles in key areas. Currently, most target recognition systems are based on devices such as fiber optics, radar, and vibration sensors. A system based on vibration sensors has the advantages of small size, low power consumption, strong concealment, easy installation, and low power consumption.
View Article and Find Full Text PDFAutomated mold defects classification in paintings: A comparison of machine learning and rule-based techniques.
PLoS One
January 2025
Faculty of Science and Engineering, School of Computer Science, University of Hull, Hull, United Kingdom.
Mold defects pose a significant risk to the preservation of valuable fine art paintings, typically arising from fungal growth in humid environments. This paper presents a novel approach for detecting and categorizing mold defects in fine art paintings. The technique leverages a feature extraction method called Derivative Level Thresholding to pinpoint suspicious regions within an image.
View Article and Find Full Text PDFUsage of Milan System in Risk Stratification of Submandibular Gland Fine Needle Aspiration Cytology.
Laryngoscope
January 2025
Department of Otorhinolaryngology, Tan Tock Seng Hospital, Singapore, Singapore.
Objectives: The Milan System for Reporting Salivary Gland Cytopathology (MSRSGC) categorizes fine needle aspiration and cytology (FNAC) results into 6 tiers and assigns a risk of malignancy (ROM) to each category. Its utility in submandibular gland lesions remains unclear. Our article aims to study (1) the utility of the MSRSGC in classifying FNAC of submandibular gland masses, (2) describe the demographics and incidence of submandibular gland malignancy in our population, and (3) analyze the accuracy of FNAC in diagnosis of benign and malignant submandibular gland tumors.
View Article and Find Full Text PDFEnhancing diagnostic accuracy in breast cancer: integrating novel machine learning approaches with enhanced image preprocessing for improved mammography analysis.
Pol J Radiol
December 2024
Nuclear Fuel Research School, Nuclear Science and Technology Research Institute, Tehran, Iran.
Purpose: This study explored the use of computer-aided diagnosis (CAD) systems to enhance mammography image quality and identify potentially suspicious areas, because mammography is the primary method for breast cancer screening. The primary aim was to find the best combination of preprocessing algorithms to enable more precise classification and interpretation of mammography images because the selected preprocessing algorithms significantly impact the effectiveness of later classification and segmentation processes.
Material And Methods: The study utilised the mini-MIAS database of mammography images and examined the impact of applying various preprocessing method combinations to differentiate between malignant and benign breast lesions.
Personalized optimization of systematic prostate biopsy core number based on mpMRI radiomics features: a large-sample retrospective analysis.
BMC Cancer
January 2025
Department of Urology, Fujian Union Hospital, Fujian Medical University, Fuzhou, 350001, Fujian Province, China.
Background: Prostate cancer (PCa) is definitively diagnosed by systematic prostate biopsy (SBx) with 13 cores. This method, however, can increase the risk of urinary retention, infection and bleeding due to the excessive number of biopsy cores.
Methods: We retrospectively analyzed 622 patients who underwent SBx with prostate multiparametric MRI (mpMRI) from two centers between January 2014 to June 2022.
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