Adaptive vector median filtering
Pattern Recognition Letters
Modified repeated median filters
Statistics and Computing
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Letters: Convex incremental extreme learning machine
Neurocomputing
Rapid and brief communication: Evolutionary extreme learning machine
Pattern Recognition
Information Sciences: an International Journal
Large-scale image classification: Fast feature extraction and SVM training
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Image enhancement based on equal area dualistic sub-image histogram equalization method
IEEE Transactions on Consumer Electronics
Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement
IEEE Transactions on Consumer Electronics
Simple adaptive median filter for the removal of impulse noise from highly corrupted images
IEEE Transactions on Consumer Electronics
A deblocking technique for block-transform compressed image using wavelet transform modulus maxima
IEEE Transactions on Image Processing
A new algorithm for image noise reduction using mathematical morphology
IEEE Transactions on Image Processing
Universal approximation using incremental constructive feedforward networks with random hidden nodes
IEEE Transactions on Neural Networks
Update strategy based on region classification using ELM for mobile object index
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Extreme Learning Machines (ELM 2011) Hangzhou, China, December 6 – 8, 2011
Face recognition with lattice independent component analysis and extreme learning machines
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Extreme Learning Machines (ELM 2011) Hangzhou, China, December 6 – 8, 2011
Segmentation of mammography by applying extreme learning machine in tumor detection
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
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Breast tumor detection in digital mammography is one of the most important methods of breast cancer prevention. Computer-aided diagnosis (CAD) based on extreme learning machine (ELM) has significant meanings for breast tumor detection as it has good generalization abilities and a high learning efficiency. In this paper, a breast tumor detection algorithm in digital mammography based on ELM is proposed. First, a median filter is used for noise reduction, and contrast enhancement of the digital mammography in data preprocessing. Next, methods of wavelet modulus maxima transform, morphological operation and region growth are used for the breast tumor edge segmentation. Then, five textural features and five morphological features are extracted. Finally, an ELM classifier is used to detect the breast tumor. Comparing breast tumor detection based on Support Vector Machines (SVM), with breast tumor detection based on ELM, not only does ELM have a better classification accuracy than SVM, but it also has a greatly improved training speed.