Trends in biomedical signal feature extraction
S Krishnan, Y Athavale - Biomedical Signal Processing and Control, 2018 - Elsevier
Signal analysis involves identifying signal behaviour, extracting linear and non-linear
properties, compression or expansion into higher or lower dimensions, and recognizing …
properties, compression or expansion into higher or lower dimensions, and recognizing …
Quantification of heterogeneity as a biomarker in tumor imaging: a systematic review
Background Many techniques are proposed for the quantification of tumor heterogeneity as
an imaging biomarker for differentiation between tumor types, tumor grading, response …
an imaging biomarker for differentiation between tumor types, tumor grading, response …
Segmentation information with attention integration for classification of breast tumor in ultrasound image
Y Luo, Q Huang, X Li - Pattern Recognition, 2022 - Elsevier
Breast cancer is one of the most common forms of cancer among women worldwide. The
development of computer-aided diagnosis (CAD) technology based on ultrasound imaging …
development of computer-aided diagnosis (CAD) technology based on ultrasound imaging …
Automated diagnosis of breast cancer using multi-modal datasets: A deep convolution neural network based approach
This paper proposes a deep convolutional neural network (CNN) model for automated
breast cancer classification from a different class of images, namely, mammograms and …
breast cancer classification from a different class of images, namely, mammograms and …
[PDF][PDF] Deep learning approaches for data augmentation and classification of breast masses using ultrasound images
W Al-Dhabyani, M Gomaa, H Khaled… - Int. J. Adv. Comput. Sci …, 2019 - academia.edu
Breast classification and detection using ultrasound imaging is considered a significant step
in computer-aided diagnosis systems. Over the previous decades, researchers have proved …
in computer-aided diagnosis systems. Over the previous decades, researchers have proved …
On combining biclustering mining and AdaBoost for breast tumor classification
Breast cancer is now considered as one of the leading causes of deaths among women all
over the world. Aiming to assist clinicians in improving the accuracy of diagnostic decisions …
over the world. Aiming to assist clinicians in improving the accuracy of diagnostic decisions …
PSOWNNs‐CNN: A Computational Radiology for Breast Cancer Diagnosis Improvement Based on Image Processing Using Machine Learning Methods
A Nomani, Y Ansari, MH Nasirpour… - Computational …, 2022 - Wiley Online Library
Early diagnosis of breast cancer is an important component of breast cancer therapy. A
variety of diagnostic platforms can provide valuable information regarding breast cancer …
variety of diagnostic platforms can provide valuable information regarding breast cancer …
Stacked deep polynomial network based representation learning for tumor classification with small ultrasound image dataset
Ultrasound imaging has been widely used for tumor detection and diagnosis. In ultrasound
based computer-aided diagnosis, feature representation is a crucial step. In recent years …
based computer-aided diagnosis, feature representation is a crucial step. In recent years …
3D shearlet-based descriptors combined with deep features for the classification of Alzheimer's disease based on MRI data
S Alinsaif, J Lang… - Computers in Biology …, 2021 - Elsevier
Alzheimer's disease (AD) is a neurodegenerative disease that afflicts millions of people
worldwide. Early detection of AD is critical, as drug trials show a promising advantage to …
worldwide. Early detection of AD is critical, as drug trials show a promising advantage to …
Segmentation and detection of breast cancer in mammograms combining wavelet analysis and genetic algorithm
Abstract In Brazil, the National Cancer Institute (INCA) reports more than 50,000 new cases
of the disease, with risk of 51 cases per 100,000 women. Radiographic images obtained …
of the disease, with risk of 51 cases per 100,000 women. Radiographic images obtained …