Combining optimal wavelet statistical texture and recurrent neural network for tumour detection and classification over MRI

Begum, S. Salma; Lakshmi, D. Rajya

doi:10.1007/s11042-020-08643-w

Cited by 44 publications

(13 citation statements)

References 28 publications

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“…Because of the feature fusion procedure, the failure rate rose. Integrating an optimum wavelet statistics structure with the recurrent neural network for tumor identification and tracking was presented by Begum and Lakshmi [ 13 ]. The MIAS dataset provided the input mammography images.…”

Section: Literature Reviewmentioning

confidence: 99%

CT-ML: Diagnosis of Breast Cancer Based on Ultrasound Images and Time-Dependent Feature Extraction Methods Using Contourlet Transformation and Machine Learning

Masjidi

Bahmani

Sharifi

et al. 2022

Computational Intelligence and Neuroscience

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Breast diseases are a group of diseases that appear in different forms. An entire group of these diseases is breast cancer. This disease is one of the most important and common diseases in women. A machine learning system has been trained to identify specific patterns using an algorithm in a machine learning system to diagnose breast cancer. Therefore, designing a feature extraction method is essential to decrease the computation time. In this article, a two-dimensional contourlet is utilized as the input image based on the Breast Cancer Ultrasound Dataset. The sub-banded contourlet coefficients are modeled using the time-dependent model. The features of the time-dependent model are considered the leading property vector. The extracted features are applied separately to determine breast cancer classes based on classification methods. The classification is performed for the diagnosis of tumor types. We used the time-dependent approach to feature contourlet sub-bands from three groups of benign, malignant, and health control test samples. The final feature of 1200 ultrasound images used in three categories is trained based on k-nearest neighbor, support vector machine, decision tree, random forest, and linear discrimination analysis approaches, and the results are recorded. The decision tree results show that the method’s sensitivity is 87.8%, 92.0%, and 87.0% for normal, benign, and malignant, respectively. The presented feature extraction method is compatible with the decision tree approach for this problem. Based on the results, the decision tree architecture with the highest accuracy is the more accurate and compatible method for diagnosing breast cancer using ultrasound images.

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Section: Literature Reviewmentioning

confidence: 99%

CT-ML: Diagnosis of Breast Cancer Based on Ultrasound Images and Time-Dependent Feature Extraction Methods Using Contourlet Transformation and Machine Learning

Masjidi

Bahmani

Sharifi

et al. 2022

Computational Intelligence and Neuroscience

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“…RNNs are one of the most important subfields of deep learning, which are used to handle sequential input [44,45]. RNN is inherently deep in time because to the sequential treatment of data.…”

Section: Classificationmentioning

confidence: 99%

“…However, in the field of image recognition, RNNs were mostly employed to create picture pixel sequences rather than for complete image recognition [18]. Since RNN's design has grown and become more efficient, it may be worthwhile to investigate whether these remarkable advancements have a direct impact on image classification [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Exploiting the Performance of Marine Predators Optimization Algorithm in Combination with Neural Network Classifiers for Breast Mass Classification

2022

IJIES

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Recently, breast cancer has achieved top position in terms of cause of mortality among women of all age groups by surpassing the lung cancer. To improve the survival rate, timely assessments are essential. Mammography is the best modality among the most widely used timely detection modalities. The radiologists' manual reading may have an impact on the accuracy of the diagnosis. As a result, the computer-aided diagnosis (CAD) systems are being developed as tools to reduce the false alarms and to increase the diagnosis accuracy. In this study, an attempt has been made to improve the diagnosis performance of the CAD systems by incorporating recently developed marine predators algorithm (MPA) in conjunction with three different neural network classifiers including feedforward neural network (FFNN), cascade forward neural network (CFNN), and recurrent neural network (RNN). Unlike other existing studies, fully digital mammogram images from INbreast dataset have been employed for testing of the system proposed in this study. Experimental results reveal that the best classification performance [Accuracy 97.34%, Sensitivity: 98.40%, Specificity: 100.00%] is obtained when MPA is used in conjunction with RNN classifier. To demonstrate the usefulness of the proposed system, the obtained results are compared with the results obtained using already invented CAD systems in previously published studies using the same dataset. The findings suggest that the proposed system is acceptable for real-time clinical applications.

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“…The RNN architecture is a type of DL algorithm that processes variable sequence of inputs using their internal states [72]. It allows a dynamic behavior derived from a feedforward neural network, which allows them to be applicable in miscellaneous tasks including speech [73], handwriting recognition [74], tumour detection with classification [75], network traffic analysis [76]- [78], text classification [79]- [82], and sentiment analysis [83]- [89].…”

Section: ) Rnn Architecturementioning

confidence: 99%

Deep Sentiment Analysis: A Case Study on Stemmed Turkish Twitter Data

Shehu

Sharif

et al. 2021

IEEE Access

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Sentiment analysis using stemmed Twitter data from various languages is an emerging research topic. In this paper, we address three data augmentation techniques namely Shift, Shuffle, and Hybrid to increase the size of the training data; and then we use three key types of deep learning (DL) models namely recurrent neural network (RNN), convolution neural network (CNN), and hierarchical attention network (HAN) to classify the stemmed Turkish Twitter data for sentiment analysis. The performance of these DL models has been compared with the existing traditional machine learning (TML) models. The performance of TML models has been affected negatively by the stemmed data, but the performance of DL models has been improved greatly with the utilization of the augmentation techniques. Based on the simulation, experimental, and statistical results analysis deeming identical datasets, it has been concluded that the TML models outperform the DL models with respect to both training-time (TTM) and runtime (RTM) complexities of the algorithms; but the DL models outperform the TML models with respect to the most important performance factors as well as the average performance rankings.

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Combining optimal wavelet statistical texture and recurrent neural network for tumour detection and classification over MRI

Cited by 44 publications

References 28 publications

CT-ML: Diagnosis of Breast Cancer Based on Ultrasound Images and Time-Dependent Feature Extraction Methods Using Contourlet Transformation and Machine Learning

CT-ML: Diagnosis of Breast Cancer Based on Ultrasound Images and Time-Dependent Feature Extraction Methods Using Contourlet Transformation and Machine Learning

Exploiting the Performance of Marine Predators Optimization Algorithm in Combination with Neural Network Classifiers for Breast Mass Classification

Deep Sentiment Analysis: A Case Study on Stemmed Turkish Twitter Data

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