2 research outputs found
Artificial Neural Network Based Breast Cancer Screening: A Comprehensive Review
Breast cancer is a common fatal disease for women. Early diagnosis and
detection is necessary in order to improve the prognosis of breast cancer
affected people. For predicting breast cancer, several automated systems are
already developed using different medical imaging modalities. This paper
provides a systematic review of the literature on artificial neural network
(ANN) based models for the diagnosis of breast cancer via mammography. The
advantages and limitations of different ANN models including spiking neural
network (SNN), deep belief network (DBN), convolutional neural network (CNN),
multilayer neural network (MLNN), stacked autoencoders (SAE), and stacked
de-noising autoencoders (SDAE) are described in this review. The review also
shows that the studies related to breast cancer detection applied different
deep learning models to a number of publicly available datasets. For comparing
the performance of the models, different metrics such as accuracy, precision,
recall, etc. were used in the existing studies. It is found that the best
performance was achieved by residual neural network (ResNet)-50 and ResNet-101
models of CNN algorithm.Comment: 13 pages, 8 figure
A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks
Breast cancer is one of the most common and deadliest cancers among women.
Since histopathological images contain sufficient phenotypic information, they
play an indispensable role in the diagnosis and treatment of breast cancers. To
improve the accuracy and objectivity of Breast Histopathological Image Analysis
(BHIA), Artificial Neural Network (ANN) approaches are widely used in the
segmentation and classification tasks of breast histopathological images. In
this review, we present a comprehensive overview of the BHIA techniques based
on ANNs. First of all, we categorize the BHIA systems into classical and deep
neural networks for in-depth investigation. Then, the relevant studies based on
BHIA systems are presented. After that, we analyze the existing models to
discover the most suitable algorithms. Finally, publicly accessible datasets,
along with their download links, are provided for the convenience of future
researchers.Comment: 25 pages,19 figure