AI Article Synopsis
Article Abstract
In this paper, an algorithm for detection of suspicious masses from mammographic images is presented. The proposed algorithm was tested on a database of 61 mammograms on which masses had previously been marked by experienced radiologists. Results show that the proposed method exhibits for mass detection, a sensitivity of 95.91%. The area under receiver operating characteristic (ROC) Az was 0.946 when enhancement of the original image was performed before detection and 0.938 otherwise. Furthermore in some cases, we could detect some masses that the radiologists were not able to mark out.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.compbiomed.2005.12.004 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Multidimensional free shape-morphing flexible neuromorphic devices with regulation at arbitrary points.
Nat Commun
January 2025
Institute of Optoelectronic Thin Film Devices and Technology, Key Laboratory of Optoelectronic Thin Film Devices and Technology of Tianjin, College of Electronic Information and Optical Engineering, National Institute for Advanced Materials, Nankai University, Tianjin, China.
Biological neural systems seamlessly integrate perception and action, a feat not efficiently replicated in current physically separated designs of neural-imitating electronics. This segregation hinders coordination and functionality within the neuromorphic system. Here, we present a flexible device tailored for neuromorphic computation and muscle actuation.
View Article and Find Full Text PDFDetection of necrotic enteritis risk through non-invasive monitoring of Clostridium perfringens in feces.
Poult Sci
January 2025
Ancera, Inc, Branford, CT 06405, USA. Electronic address:
Necrotic enteritis (NE), caused by the gram-positive, anaerobic bacterium, Clostridium perfringens, results in an estimated $6 billion in annual economic losses to the global poultry industry. C. perfringens is part of the normal microflora of the poultry gastrointestinal tract, but damage to the intestinal epithelium can lead to increased cell proliferation and production of toxins which gives rise to disease.
View Article and Find Full Text PDFResViT FusionNet Model: An explainable AI-driven approach for automated grading of diabetic retinopathy in retinal images.
Comput Biol Med
January 2025
Department of Creative Technologies, Air University, Islamabad, 44000, Pakistan. Electronic address:
Background And Objective: Diabetic Retinopathy (DR) is a serious diabetes complication that can cause blindness if not diagnosed in its early stages. Manual diagnosis by ophthalmologists is labor-intensive and time-consuming, particularly in overburdened healthcare systems. This highlights the need for automated, accurate, and personalized machine learning approaches for early DR detection and treatment.
View Article and Find Full Text PDFDeep Equilibrium Unfolding Learning for Noise Estimation and Removal in Optical Molecular Imaging.
Comput Med Imaging Graph
January 2025
CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; National Key Laboratory of Kidney Diseases, Beijing 100853, China. Electronic address:
In clinical optical molecular imaging, the need for real-time high frame rates and low excitation doses to ensure patient safety inherently increases susceptibility to detection noise. Faced with the challenge of image degradation caused by severe noise, image denoising is essential for mitigating the trade-off between acquisition cost and image quality. However, prevailing deep learning methods exhibit uncontrollable and suboptimal performance with limited interpretability, primarily due to neglecting underlying physical model and frequency information.
View Article and Find Full Text PDFGlaucoma detection and staging from visual field images using machine learning techniques.
PLoS One
January 2025
School of Optometry and Vision Science, UNSW Sydney, Sydney, New South Wales, Australia.
Purpose: In this study, we investigated the performance of deep learning (DL) models to differentiate between normal and glaucomatous visual fields (VFs) and classify glaucoma from early to the advanced stage to observe if the DL model can stage glaucoma as Mills criteria using only the pattern deviation (PD) plots. The DL model results were compared with a machine learning (ML) classifier trained on conventional VF parameters.
Methods: A total of 265 PD plots and 265 numerical datasets of Humphrey 24-2 VF images were collected from 119 normal and 146 glaucomatous eyes to train the DL models to classify the images into four groups: normal, early glaucoma, moderate glaucoma, and advanced glaucoma.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!