Classification of mammogram images using multiscale all convolutional neural network (MA-CNN)
Breast cancer is one of the leading causes of cancer death among women in worldwide.
Early diagnosis of breast cancer improves the chance of survival by aiding proper clinical …
Early diagnosis of breast cancer improves the chance of survival by aiding proper clinical …
Data mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy features
Eye images provide an insight into important parts of the visual system, and also indicate the
health of the entire human body. Glaucoma is one of the most common causes of blindness …
health of the entire human body. Glaucoma is one of the most common causes of blindness …
Abnormality detection in mammography using deep convolutional neural networks
Breast cancer is the most common cancer in women worldwide. The most common
screening technology is mammography. To reduce the cost and workload of radiologists, we …
screening technology is mammography. To reduce the cost and workload of radiologists, we …
[HTML][HTML] Feature fusion and Ensemble learning-based CNN model for mammographic image classification
In recent times, the world has faced an alarming situation regarding breast cancer patients.
The early diagnosis of this deadly disease can make the treatment more accessible and …
The early diagnosis of this deadly disease can make the treatment more accessible and …
Computer-aided diagnosis of malignant mammograms using Zernike moments and SVM
This work is directed toward the development of a computer-aided diagnosis (CAD) system
to detect abnormalities or suspicious areas in digital mammograms and classify them as …
to detect abnormalities or suspicious areas in digital mammograms and classify them as …
A context-sensitive deep learning approach for microcalcification detection in mammograms
J Wang, Y Yang - Pattern recognition, 2018 - Elsevier
A challenging issue in computerized detection of clustered microcalcifications (MCs) is the
frequent occurrence of false positives (FPs) caused by local image patterns that resemble …
frequent occurrence of false positives (FPs) caused by local image patterns that resemble …
Application of Gabor wavelet and Locality Sensitive Discriminant Analysis for automated identification of breast cancer using digitized mammogram images
Breast cancer is one of the prime causes of death in women. Early detection may help to
improve the survival rate to a great extent. Mammography is considered as one of the most …
improve the survival rate to a great extent. Mammography is considered as one of the most …
Multiscale edge detection based on Gaussian smoothing and edge tracking
The human vision is usually considered a multiscale, hierarchical knowledge extraction
system. Inspired by this fact, multiscale techniques for computer vision perform a sequential …
system. Inspired by this fact, multiscale techniques for computer vision perform a sequential …
Multiple-instance learning for anomaly detection in digital mammography
G Quellec, M Lamard, M Cozic… - Ieee transactions on …, 2016 - ieeexplore.ieee.org
This paper describes a computer-aided detection and diagnosis system for breast cancer,
the most common form of cancer among women, using mammography. The system relies on …
the most common form of cancer among women, using mammography. The system relies on …
Topological modeling and classification of mammographic microcalcification clusters
Goal: The presence of microcalcification clusters is a primary sign of breast cancer; however,
it is difficult and time consuming for radiologists to classify microcalcifications as malignant …
it is difficult and time consuming for radiologists to classify microcalcifications as malignant …