301 research outputs found
Hand-Based Biometric Analysis
Hand-based biometric analysis systems and techniques provide robust hand-based identification and verification. An image of a hand is obtained, which is then segmented into a palm region and separate finger regions. Acquisition of the image is performed without requiring particular orientation or placement restrictions. Segmentation is performed without the use of reference points on the images. Each segment is analyzed by calculating a set of Zernike moment descriptors for the segment. The feature parameters thus obtained are then fused and compared to stored sets of descriptors in enrollment templates to arrive at an identity decision. By using Zernike moments, and through additional manipulation, the biometric analysis is invariant to rotation, scale, or translation or an input image. Additionally, the analysis uses re-use of commonly seen terms in Zernike calculations to achieve additional efficiencies over traditional Zernike moment calculation
Hand-Based Biometric Analysis
Hand-based biometric analysis systems and techniques are described which provide robust hand-based identification and verification. An image of a hand is obtained, which is then segmented into a palm region and separate finger regions. Acquisition of the image is performed without requiring particular orientation or placement restrictions. Segmentation is performed without the use of reference points on the images. Each segment is analyzed by calculating a set of Zernike moment descriptors for the segment. The feature parameters thus obtained are then fused and compared to stored sets of descriptors in enrollment templates to arrive at an identity decision. By using Zernike moments, and through additional manipulation, the biometric analysis is invariant to rotation, scale, or translation or an in put image. Additionally, the analysis utilizes re-use of commonly-seen terms in Zernike calculations to achieve additional efficiencies over traditional Zernike moment calculation
ConnectedUNets++: Mass Segmentation from Whole Mammographic Images
Deep learning has made a breakthrough in medical image segmentation in recent
years due to its ability to extract high-level features without the need for
prior knowledge. In this context, U-Net is one of the most advanced medical
image segmentation models, with promising results in mammography. Despite its
excellent overall performance in segmenting multimodal medical images, the
traditional U-Net structure appears to be inadequate in various ways. There are
certain U-Net design modifications, such as MultiResUNet, Connected-UNets, and
AU-Net, that have improved overall performance in areas where the conventional
U-Net architecture appears to be deficient. Following the success of UNet and
its variants, we have presented two enhanced versions of the Connected-UNets
architecture: ConnectedUNets+ and ConnectedUNets++. In ConnectedUNets+, we have
replaced the simple skip connections of Connected-UNets architecture with
residual skip connections, while in ConnectedUNets++, we have modified the
encoder-decoder structure along with employing residual skip connections. We
have evaluated our proposed architectures on two publicly available datasets,
the Curated Breast Imaging Subset of Digital Database for Screening Mammography
(CBIS-DDSM) and INbreast.Comment: Results are to be update
Deep Learning Hyperparameter Optimization for Breast Mass Detection in Mammograms
Accurate breast cancer diagnosis through mammography has the potential to
save millions of lives around the world. Deep learning (DL) methods have shown
to be very effective for mass detection in mammograms. Additional improvements
of current DL models will further improve the effectiveness of these methods. A
critical issue in this context is how to pick the right hyperparameters for DL
models. In this paper, we present GA-E2E, a new approach for tuning the
hyperparameters of DL models for brest cancer detection using Genetic
Algorithms (GAs). Our findings reveal that differences in parameter values can
considerably alter the area under the curve (AUC), which is used to determine a
classifier's performance
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