A Discrete Wavelet Based Feature Extraction and Hybrid Classification Technique for Microarray Data Analysis

Bennet, Jaison; Ganaprakasam, Chilambuchelvan; Kannan, A.

doi:10.1155/2014/195470

Cited by 27 publications

(12 citation statements)

References 16 publications

(23 reference statements)

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“…We employed DWT and DCT as they are popular feature extraction method based on textural analysis. One of the main benefit of DCT is its capability to spatially alter to characteristics of an image for instance discontinuities and changing frequency manner (Bennet, Arul Ganaprakasam & Arputharaj, 2014). It offers time-frequency representation of an image.…”

Section: Feature Extraction Reduction and Fusion Stepmentioning

confidence: 99%

GASTRO-CADx: a three stages framework for diagnosing gastrointestinal diseases

Attallah¹,

Sharkas²

2021

PeerJ Computer Science

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Gastrointestinal (GI) diseases are common illnesses that affect the GI tract. Diagnosing these GI diseases is quite expensive, complicated, and challenging. A computer-aided diagnosis (CADx) system based on deep learning (DL) techniques could considerably lower the examination cost processes and increase the speed and quality of diagnosis. Therefore, this article proposes a CADx system called Gastro-CADx to classify several GI diseases using DL techniques. Gastro-CADx involves three progressive stages. Initially, four different CNNs are used as feature extractors to extract spatial features. Most of the related work based on DL approaches extracted spatial features only. However, in the following phase of Gastro-CADx, features extracted in the first stage are applied to the discrete wavelet transform (DWT) and the discrete cosine transform (DCT). DCT and DWT are used to extract temporal-frequency and spatial-frequency features. Additionally, a feature reduction procedure is performed in this stage. Finally, in the third stage of the Gastro-CADx, several combinations of features are fused in a concatenated manner to inspect the effect of feature combination on the output results of the CADx and select the best-fused feature set. Two datasets referred to as Dataset I and II are utilized to evaluate the performance of Gastro-CADx. Results indicated that Gastro-CADx has achieved an accuracy of 97.3% and 99.7% for Dataset I and II respectively. The results were compared with recent related works. The comparison showed that the proposed approach is capable of classifying GI diseases with higher accuracy compared to other work. Thus, it can be used to reduce medical complications, death-rates, in addition to the cost of treatment. It can also help gastroenterologists in producing more accurate diagnosis while lowering inspection time.

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Section: Feature Extraction Reduction and Fusion Stepmentioning

confidence: 99%

GASTRO-CADx: a three stages framework for diagnosing gastrointestinal diseases

Attallah¹,

Sharkas²

2021

PeerJ Computer Science

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“…Discrete Wavelet Transform (DWT) is a feature extraction method that processes the signals for generating genes to be treated [14]. In this research, the microarray feature plays the signal input in the dimension reduction process in DWT.…”

Section: B) Dimension Reductionmentioning

confidence: 99%

Discrete Wavelet Transform (DWT) and Random Forest for Cancer Detection Based on Microarray Data Classification

2020

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Cancer is one of the leading causes of death worldwide. According to the World Health Organization (WHO) in 2018, about 9.6 million deaths caused by cancer. DNA microarray technology has played an important role in analyzing and diagnosing cancer. However, microarray data has a large data dimensions resulting in the accuracy of the Random Forest are not optimal. In this paper, the Discrete Wavelet Transform (DWT) is selected as a feature extraction method. Based on the simulation, the dimension can be reduced and improve the accuracy of classification up to 8% - 20%. DWT approximation coefficient can improve accuracy better than detailed coefficients for data on colon cancer 100%, lung cancer 100%, ovarian 100%, prostate tumor 85.71%, and central nervous system 83.33%.

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“…Hence, they employed the wavelet transform to select the relevant features for the classification. Likewise, Bennet et al [6] also used a discrete wavelet technique for feature extraction and then employed a hybrid classifier for micro-array data analysis for Cancer. The method which was proposed in their work was based on the naive Bayes, support vector machine (SVM), and K nearest neighbor (KNN).…”

Section: Introductionmentioning

confidence: 99%

Wavelets Based Feature Extraction With PCA For Predicting Autism In Neonates Using Navïe Bayes Classifier

Mensah

Amoako-Yirenkyi

Frempong

et al. 2021

Preprint

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Background: Current studies show early interventions of autism increase significant long-term positive effects, symptoms and, later skills. Currently, These interventions are based on the use of an early diagnostic test. Existing methods for diagnosing Autism Spectrum Disorders (ASDs) such as cognitive tests, Intelligence Quotient, and standardized tests like the Autism Diagnostic Observation Schedule (ADOS) are functionally limited since they rely on child development for diagnoses. The standard is that a child must be at least three(3) years to undergo these tests. Accurate diagnosis is only possible after this period, and this may contribute to delayed diagnosis with an overall effect on the health system. In this era of increasing genetic data, it is possible to infer the genetic patterns of the disorder. This study introduces a novel and rigorous approach for predicting ASDs in neonates and their subsequent severity by identifying significant genes that contribute to the disorder. Methods: We used a wavelet transform and t-test to identify the significant genes that contribute to the disease. We subsequently employed the Naive Bayes classifier in the prediction of the autistic status of the neonate. Additionally, Principal Component Analysis (PCA) was employed to remove all the dependencies among the genes to enhance classification. Finally, we used the K-means clustering method to predict the severity level of the disease in the neonate. Results: Up to 200 differentially expressed genes were identified and used for predicting the ASD status of the child with a classification accuracy of 95.91%. Also, the results of the K-means demonstrated that the higher the mean of the cluster, the more severe the disease would be among that corresponding group. Optimizing and implementing these models in clinical settings may significantly reduce the health burden of ASDs.

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A Discrete Wavelet Based Feature Extraction and Hybrid Classification Technique for Microarray Data Analysis

Cited by 27 publications

References 16 publications

GASTRO-CADx: a three stages framework for diagnosing gastrointestinal diseases

GASTRO-CADx: a three stages framework for diagnosing gastrointestinal diseases

Discrete Wavelet Transform (DWT) and Random Forest for Cancer Detection Based on Microarray Data Classification

Wavelets Based Feature Extraction With PCA For Predicting Autism In Neonates Using Navïe Bayes Classifier

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