A study of the suitability of autoencoders for preprocessing data in breast cancer experimentation

Macías-García, Laura; Luna-Romera, José María; García-Gutiérrez, Jorge; Martínez-Ballesteros, M.; Santos, José Cristóbal Riquelme; González‐Cámpora, Ricardo

doi:10.1016/j.jbi.2017.06.020

Cited by 19 publications

(3 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, several big data scene methods based on MapReduce have been applied for traditional tasks. The parallel-based SVM algorithms have been applied in the field of high performance computing [9]. Several parallel KNN algorithms based on Spark have been proposed in [10].…”

Section: The Researchmentioning

confidence: 99%

A Novel Ensemble Forecasting Algorithm based on Distributed Deep Learning Network

Fen¹,

Tian²,

Ma³

et al. 2019

Int J Performability Eng

View full text Add to dashboard Cite

This paper proposes an ensemble model based on distribution deep learning network. The ensemble model is composed of deep belief network (DBN) for reconstructing original data, and the bidirectional long short-term memory (BLSTM) method is used for prediction due to its good results in big data applications. The dynamic weighting strategies are proposed and applied to the sub models of the ensemble by a weighted least square method. The weight update with variable training sets and the predictions for each ensemble are obtained from the distributed computing engine Apache Spark. The performance of the proposed model is evaluated on wind data on the wind farm of the Hexi Corridor in China. The simulation results show that the dynamic ensemble algorithm performs well, which is a very valuable result for the forecasting of big data time series. Furthermore, the results are successfully compared with back propagation neural Network (BPNN), LSTM, BLSTM, and stacked LSTMs with memory between batches (SBLSTM), improving the accuracy of prediction.

show abstract

Section: The Researchmentioning

confidence: 99%

A Novel Ensemble Forecasting Algorithm based on Distributed Deep Learning Network

Fen¹,

Tian²,

Ma³

et al. 2019

Int J Performability Eng

View full text Add to dashboard Cite

show abstract

“…Convolutional neural networks (CNNs), deep-belief networks (DBNs), auto-encoders (AEs), and recurrent neural networks (RNNs) are the four architectures under question. The disorder detection software commonly uses these structures [17,18]. The year-wise growth and research distribution for DL articles in HCS is depicted in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Deep Learning Methods for Healthcare Sector - Medical Imaging Disease Detection

Sahu,

Kashyap,

Bhatia

et al. 2023

Contemp. Math.

View full text Add to dashboard Cite

In this paper, artificial intelligence (AI) and the ideas of machine learning (ML) and deep learning (DL) are introduced gradually. Applying ML techniques like deep neural network (DNN) models has grown in popularity in recent years due to the complexity of healthcare data, which has been increasing. To extract hidden patterns and some other crucial information from the enormous amount of health data, which traditional analytics are unable to locate in a fair amount of time, ML approaches offer cost-effective and productive models for data analysis. We are encouraged to pursue this work because of the quick advancements made in DL approaches. The idea of DL is developing from its theoretical foundations to its applications. Modern ML models that are widely utilized in academia and industry, mostly in image classification and natural language processing, including DNN. Medical imaging technologies, medical healthcare data processing, medical disease diagnostics, and general healthcare all stand to greatly benefit from these developments. We have two goals: first, to conduct a survey on DL techniques for medical pictures, and second, to develop DL-based approaches for image classification. This paper is mainly targeted towards understanding the feasibility and different processes that could be adopted for medical image classification; for this, we perform a systematic literature review. A review of various existing techniques in terms of medical image classification indicates some shortcomings that have an impact on the performance of the whole model. This study aims to explore the existing DL approaches, challenges, brief comparisons, and applicability of different medical image processing are also studied and presented. The adoption of fewer datasets, poor use of temporal information, and reduced classification accuracy all contribute to the lower performance model, which is addressed. The study provides a clear explanation of contemporary developments, cutting-edge learning tools, and platforms for DL techniques.

show abstract

“…They elicited nine decision rules from a thoracic surgery data set including 470 samples and 16 features for medical utilization, for predicting post-operative survival expectancy in lung cancer patients. Iraji (2017) has implemented a multi-layer architecture of sub-adaptive neuro-fuzzy inference system (MLA-ANFIS) with various combinations of multiple input features, regression, neural networks and an extreme learning machine (Macías-García et al, 2017) based on a thoracic surgery data set with 16 input features for prediction of 1-year post-operative survival expectancy in thoracic lung cancer surgery; ELM (wave kernel) has good performance.…”

Section: Introductionmentioning

confidence: 99%

Deep stacked sparse auto-encoders for prediction of post-operative survival expectancy in thoracic lung cancer surgery

Iraji¹

2019

J Appl Biomed

View full text Add to dashboard Cite

Lung cancer is the leading cause of cancer death in men and women. The prognostic value of survival after lung cancer surgery has an important role in decision-making for surgeons and patients. The combination of clinical features and CT scan information for diagnosis, treatment and survival of patients with lung cancer increases the accuracy of prediction using machine learning. Therefore, creating a computer intelligent method with low error and high accuracy to predict survival is an important challenge, and it is beneficial for decreasing mortality from lung cancer, and for planning treatment. In this work, we implemented a deep stacked sparse auto-encoder (DSSAE) approach on a thoracic surgery data set for 470 patients, and our results contributing to deep learning based on 16 features were more precise than other suggested techniques for predicting postoperative survival expectancy in thoracic lung cancer surgery. The proposed method achieved a sensitivity of 94%, specificity of 82.86% and g-mean of 88.25%.

show abstract

A study of the suitability of autoencoders for preprocessing data in breast cancer experimentation

Cited by 19 publications

References 51 publications

A Novel Ensemble Forecasting Algorithm based on Distributed Deep Learning Network

A Novel Ensemble Forecasting Algorithm based on Distributed Deep Learning Network

Analysis of Deep Learning Methods for Healthcare Sector - Medical Imaging Disease Detection

Deep stacked sparse auto-encoders for prediction of post-operative survival expectancy in thoracic lung cancer surgery

Contact Info

Product

Resources

About