Diagnosis and classification of cancer using hybrid model based on ReliefF and convolutional neural network

Kılıçarslan, Serhat; Adem, Kemal; Çelik, Mete

doi:10.1016/j.mehy.2020.109577

Cited by 75 publications

(31 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In [ 11 ], the authors proposed to learn the feature presentations using a Neural Network followed by another classification network. Unsupervised clustering or Deep Autoencoder is jointly trained with a classification network [ 13 , 32 , 33 , 55 ]. However, these methods are generally applied to datasets with relatively small features where, the computational cost increases linearly with the number of features and they require more training samples to converge.…”

Section: Discussionmentioning

confidence: 99%

“…Liu et al [ 34 ] combined Deep Neural Network with an incremental way to select SNPs and multiple Dropouts regularization techniques. Kilicarslan et al [ 32 ] used a hybrid model consisting of Relief and stacked Autoencoder as dimensionality reduction technique followed by Support Vector Machines (SVM) and Convolutional Neural Networks (CNNs) for diagnosis and classification of cancer samples. Khan et al [ 35 ] used PCA and Neural Network to identify relevant genes and classify cancer samples.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

New neural network classification method for individuals ancestry prediction from SNPs data

Soumaré

Rezgui²,

Gmati

et al. 2021

BioData Mining

View full text Add to dashboard Cite

Artificial Neural Network (ANN) algorithms have been widely used to analyse genomic data. Single Nucleotide Polymorphisms(SNPs) represent the genetic variations, the most common in the human genome, it has been shown that they are involved in many genetic diseases, and can be used to predict their development. Developing ANN to handle this type of data can be considered as a great success in the medical world. However, the high dimensionality of genomic data and the availability of a limited number of samples can make the learning task very complicated. In this work, we propose a New Neural Network classification method based on input perturbation. The idea is first to use SVD to reduce the dimensionality of the input data and to train a classification network, which prediction errors are then reduced by perturbing the SVD projection matrix. The proposed method has been evaluated on data from individuals with different ancestral origins, the experimental results have shown the effectiveness of the proposed method. Achieving up to 96.23% of classification accuracy, this approach surpasses previous Deep learning approaches evaluated on the same dataset.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

New neural network classification method for individuals ancestry prediction from SNPs data

Soumaré

Rezgui²,

Gmati

et al. 2021

BioData Mining

View full text Add to dashboard Cite

show abstract

“…ReliefF is a dimension reduction method developed by Kira and Rendell, which can help remove unnecessary attributes from the data set and save storage space, thus reducing computational complexity and saving model training time. In 1994, the ReliefF model was improved by enhancing the noise resistance in the dataset and making it suitable for multi-class problems by ignoring missing data [ 24 ]. ReliefF aims to reveal the correlations and consistencies present in the attributes of the dataset.…”

Section: Methodsmentioning

confidence: 99%

“…Support vector machines (SVMs) are supervised learning methods developed by Vapnik based on statistical learning theory [ 24 ]. SVM performs the learning process with the dataset divided into training and test sets.…”

Section: Methodsmentioning

confidence: 99%

“…Hence, when applied to multi-classification, the difficulty and complexity of training accordingly increase in parallel with increasing number of sample categories. Reducing the amount of calculation and computational complexity is a known problem for SVMs, requiring new research solutions [ 24 ]. Herein, we propose to utilize the ReliefF algorithm to reduce the dimensionality of the sample data.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid ensemble model for differential diagnosis between COVID-19 and common viral pneumonia by chest X-ray radiograph

Jin

Dong

et al. 2021

Computers in Biology and Medicine

View full text Add to dashboard Cite

Background Chest X-ray radiography (CXR) has been widely considered as an accessible, feasible, and convenient method to evaluate suspected patients’ lung involvement during the COVID-19 pandemic. However, with the escalating number of suspected cases, traditional diagnosis via CXR fails to deliver results within a short period of time. Therefore, it is crucial to employ artificial intelligence (AI) to enhance CXRs for obtaining quick and accurate diagnoses. Previous studies have reported the feasibility of utilizing deep learning methods to screen for COVID-19 using CXR and CT results. However, these models only use a single deep learning network for chest radiograph detection; the accuracy of this approach required further improvement. Methods In this study, we propose a three-step hybrid ensemble model, including a feature extractor, a feature selector, and a classifier. First, a pre-trained AlexNet with an improved structure extracts the original image features. Then, the ReliefF algorithm is adopted to sort the extracted features, and a trial-and-error approach is used to select the n most important features to reduce the feature dimension. Finally, an SVM classifier provides classification results based on the n selected features. Results Compared to five existing models (InceptionV3: 97.916 ± 0.408%; SqueezeNet: 97.189 ± 0.526%; VGG19: 96.520 ± 1.220%; ResNet50: 97.476 ± 0.513%; ResNet101: 98.241 ± 0.209%), the proposed model demonstrated the best performance in terms of overall accuracy rate (98.642 ± 0.398%). Additionally, compared to the existing models, the proposed model demonstrates a considerable improvement in classification time efficiency (SqueezeNet: 6.602 ± 0.001s; InceptionV3: 12.376 ± 0.002s; ResNet50: 10.952 ± 0.001s; ResNet101: 18.040 ± 0.002s; VGG19: 16.632 ± 0.002s; proposed model: 5.917 ± 0.001s). Conclusion The model proposed in this article is practical and effective, and can provide high-precision COVID-19 CXR detection. We demonstrated its suitability to aid medical professionals in distinguishing normal CXRs, viral pneumonia CXRs and COVID-19 CXRs efficiently on small sample sizes.

show abstract

Enhancing Feature Selection Through Metaheuristic Hybrid Cuckoo Search and Harris Hawks Optimization for Cancer Classification

Yaqoob,

Verma,

Aziz

et al. 2024

Metaheuristics for Machine Learning

View full text Add to dashboard Cite

Diagnosis and classification of cancer using hybrid model based on ReliefF and convolutional neural network

Cited by 75 publications

References 42 publications

New neural network classification method for individuals ancestry prediction from SNPs data

New neural network classification method for individuals ancestry prediction from SNPs data

Hybrid ensemble model for differential diagnosis between COVID-19 and common viral pneumonia by chest X-ray radiograph

Enhancing Feature Selection Through Metaheuristic Hybrid Cuckoo Search and Harris Hawks Optimization for Cancer Classification

Contact Info

Product

Resources

About