Improved Breast Cancer Classification Through Combining Graph Convolutional Network and Convolutional Neural Network

Zhang, Yudong; Satapathy, Suresh Chandra; Guttery, David S.; Górriz, Juan Manuel; Wang, Shuihua

doi:10.1016/j.ipm.2020.102439

Cited by 259 publications

(160 citation statements)

References 26 publications

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“…For abnormal breast tissue detection, the aim is to not only learn the image-level representation automatically, but also the relation-aware representation to more accurately detect abnormal masses using mammography. Zhang et al [ 121 ] fused a CNN pipeline with a GCN pipeline to attain superior performance in classifying six abnormal types in the mini-MIAS dataset [ 122 ]. First, a CNN extracts individual image-level features; then, a GCN estimates a relation-aware representation.…”

Section: Case Studies Of Gnn For Medical Diagnosis and Analysismentioning

confidence: 99%

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

Ahmedt-Aristizabal

Armin

Denman

et al. 2021

Sensors

134

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With the advances of data-driven machine learning research, a wide variety of prediction problems have been tackled. It has become critical to explore how machine learning and specifically deep learning methods can be exploited to analyse healthcare data. A major limitation of existing methods has been the focus on grid-like data; however, the structure of physiological recordings are often irregular and unordered, which makes it difficult to conceptualise them as a matrix. As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interacting nodes connected by edges whose weights can be determined by either temporal associations or anatomical junctions. In this survey, we thoroughly review the different types of graph architectures and their applications in healthcare. We provide an overview of these methods in a systematic manner, organized by their domain of application including functional connectivity, anatomical structure, and electrical-based analysis. We also outline the limitations of existing techniques and discuss potential directions for future research.

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Section: Case Studies Of Gnn For Medical Diagnosis and Analysismentioning

confidence: 99%

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

Ahmedt-Aristizabal

Armin

Denman

et al. 2021

Sensors

134

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“…DL were also used to perform breast cancer classification. A new method called BDR-CNN-CGN was used to perform classification of breast cancer types, the results showed improved detection rates (accuracy 96.10%) compared to other neural network models [35]. A CNN was also used in order to perform COVID-19 diagnosis.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic Detection of Photovoltaic Farms Using Satellite Imagery and Convolutional Neural Networks

Ιωάννου¹,

Myronidis

2021

Sustainability

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The number of solar photovoltaic (PV) arrays in Greece has increased rapidly during the recent years. As a result, there is an increasing need for high quality updated information regarding the status of PV farms. This information includes the number of PV farms, power capacity and the energy generated. However, access to this data is obsolete, mainly due to the fact that there is a difficulty tracking PV investment status (from licensing to investment completion and energy production). This article presents a novel approach, which uses free access high resolution satellite imagery and a deep learning algorithm (a convolutional neural network—CNN) for the automatic detection of PV farms. Furthermore, in an effort to create an algorithm capable of generalizing better, all the current locations with installed PV farms (data provided from the Greek Energy Regulator Authority) in the Greek Territory (131,957 km2) were used. According to our knowledge this is the first time such an algorithm is used in order to determine the existence of PV farms and the results showed satisfying accuracy.

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“…The accuracy achieved was 91% and 83%, using CNN and CLSTM, respectively. Reference [26] combined graph convolutional network (GCN) and convolutional neural network (CNN) to analyze breast mammograms with an accuracy of 96.10 ± 1.60%. Reference [27] also used deep learning on histopathological images for breast cancer subtyping.…”

Section: Introductionmentioning

confidence: 99%

A Cascade Deep Forest Model for Breast Cancer Subtype Classification Using Multi-Omics Data

El-Nabawy¹,

Belal

El-Bendary

2021

Mathematics

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Automated diagnosis systems aim to reduce the cost of diagnosis while maintaining the same efficiency. Many methods have been used for breast cancer subtype classification. Some use single data source, while others integrate many data sources, the case that results in reduced computational performance as opposed to accuracy. Breast cancer data, especially biological data, is known for its imbalance, with lack of extensive amounts of histopathological images as biological data. Recent studies have shown that cascade Deep Forest ensemble model achieves a competitive classification accuracy compared with other alternatives, such as the general ensemble learning methods and the conventional deep neural networks (DNNs), especially for imbalanced training sets, through learning hyper-representations through using cascade ensemble decision trees. In this work, a cascade Deep Forest is employed to classify breast cancer subtypes, IntClust and Pam50, using multi-omics datasets and different configurations. The results obtained recorded an accuracy of 83.45% for 5 subtypes and 77.55% for 10 subtypes. The significance of this work is that it is shown that using gene expression data alone with the cascade Deep Forest classifier achieves comparable accuracy to other techniques with higher computational performance, where the time recorded is about 5 s for 10 subtypes, and 7 s for 5 subtypes.

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Improved Breast Cancer Classification Through Combining Graph Convolutional Network and Convolutional Neural Network

Cited by 259 publications

References 26 publications

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

Automatic Detection of Photovoltaic Farms Using Satellite Imagery and Convolutional Neural Networks

A Cascade Deep Forest Model for Breast Cancer Subtype Classification Using Multi-Omics Data

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