Involvement of machine learning for breast cancer image classification: a survey
AA Nahid, Y Kong - Computational and mathematical methods …, 2017 - Wiley Online Library
Breast cancer is one of the largest causes of women's death in the world today. Advance
engineering of natural image classification techniques and Artificial Intelligence methods …
engineering of natural image classification techniques and Artificial Intelligence methods …
Improved breast cancer classification through combining graph convolutional network and convolutional neural network
Aim In a pilot study to improve detection of malignant lesions in breast mammograms, we
aimed to develop a new method called BDR-CNN-GCN, combining two advanced neural …
aimed to develop a new method called BDR-CNN-GCN, combining two advanced neural …
A comprehensive review on multi-organs tumor detection based on machine learning
Tumor is comprised of abnormally growing regions that is dangerous for human survival.
Therefore, early stage tumor detection is useful for increase of survival rate although it is …
Therefore, early stage tumor detection is useful for increase of survival rate although it is …
A framework for breast cancer classification using multi-DCNNs
Background Deep learning (DL) is the fastest-growing field of machine learning (ML). Deep
convolutional neural networks (DCNN) are currently the main tool used for image analysis …
convolutional neural networks (DCNN) are currently the main tool used for image analysis …
Abnormal breast identification by nine-layer convolutional neural network with parametric rectified linear unit and rank-based stochastic pooling
YD Zhang, C Pan, X Chen, F Wang - Journal of computational science, 2018 - Elsevier
Aim Abnormal breast appears similar as dense breast in mammography, which makes it a
challenge for radiologists to identify. Scholars have proposed numerous computer-vision …
challenge for radiologists to identify. Scholars have proposed numerous computer-vision …
Single slice based detection for Alzheimer's disease via wavelet entropy and multilayer perceptron trained by biogeography-based optimization
Detection of Alzheimer's disease (AD) from magnetic resonance images can help
neuroradiologists to make decision rapidly and avoid missing slight lesions in the brain …
neuroradiologists to make decision rapidly and avoid missing slight lesions in the brain …
Utilization of DenseNet201 for diagnosis of breast abnormality
As one of the leading killers of females, breast cancer has become one of the heated
research topics in the community of clinical medical science and computer science. In the …
research topics in the community of clinical medical science and computer science. In the …
ResNet-SCDA-50 for breast abnormality classification
(Aim) Breast cancer is the most common cancer in women and the second most common
cancer worldwide. With the rapid advancement of deep learning, the early stages of breast …
cancer worldwide. With the rapid advancement of deep learning, the early stages of breast …
[HTML][HTML] Benign and malignant breast tumor classification in ultrasound and mammography images via fusion of deep learning and handcraft features
C Cruz-Ramos, O García-Avila, JA Almaraz-Damian… - Entropy, 2023 - mdpi.com
Breast cancer is a disease that affects women in different countries around the world. The
real cause of breast cancer is particularly challenging to determine, and early detection of …
real cause of breast cancer is particularly challenging to determine, and early detection of …
Abnormal breast detection in mammogram images by feed-forward neural network trained by Jaya algorithm
(Aim) Abnormal breast can be diagnosed using the digital mammography. Traditional
manual interpretation method cannot yield high accuracy.(Method) In this study, we …
manual interpretation method cannot yield high accuracy.(Method) In this study, we …