Combining Wavelet Texture Features and Deep Neural Network for Tumor Detection and Segmentation Over MRI

Preethi, S.; P, Aishwarya

doi:10.1515/jisys-2017-0090

Cited by 12 publications

(8 citation statements)

References 15 publications

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“…Diagnostics 2021, 12, x 3 of 12 [15], elephant herding optimisation (EHO) [16][17][18], and many others [19][20][21][22]. Several swarm intelligence and evolutionary algorithms have been applied to the ANN hyperparameters' optimisation to develop an automatic framework that will generate an optimal or near-optimal ANN structure for solving a specific problem.…”

Section: Proposed Methods Of Cancer Diagnosismentioning

confidence: 99%

“…The method can robustly segment the nuclei with reduced computational complexity and possesses an average error rate of less than 0.7% but does not work well on nuclei clusters. Some of the existing works that implement swarm algorithms for optimising the hyperparameters of networks include artificial bee colony (ABC) [ 9 ], ant colony optimisation (ACO) [ 10 ], the firefly algorithm (FA) [ 11 , 12 ], cuckoo search (CS) [ 13 ], the bat algorithm (BA) [ 14 ], the whale optimisation algorithm (WOA) [ 15 ], elephant herding optimisation (EHO) [ 16 , 17 , 18 ], and many others [ 19 , 20 , 21 , 22 ]. Several swarm intelligence and evolutionary algorithms have been applied to the ANN hyperparameters’ optimisation to develop an automatic framework that will generate an optimal or near-optimal ANN structure for solving a specific problem.…”

Section: Literature Surveymentioning

confidence: 99%

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Cancer Diagnosis of Microscopic Biopsy Images Using a Social Spider Optimisation-Tuned Neural Network

Balaji

Chidambaram

2021

Diagnostics

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One of the most dangerous diseases that threaten people is cancer. If diagnosed in earlier stages, cancer, with its life-threatening consequences, has the possibility of eradication. In addition, accuracy in prediction plays a significant role. Hence, developing a reliable model that contributes much towards the medical community in the early diagnosis of biopsy images with perfect accuracy comes to the forefront. This article aims to develop better predictive models using multivariate data and high-resolution diagnostic tools in clinical cancer research. This paper proposes the social spider optimisation (SSO) algorithm-tuned neural network to classify microscopic biopsy images of cancer. The significance of the proposed model relies on the effective tuning of the weights of the neural network classifier by the SSO algorithm. The performance of the proposed strategy is analysed with performance metrics such as accuracy, sensitivity, specificity, and MCC measures, and the attained results are 95.9181%, 94.2515%, 97.125%, and 97.68%, respectively, which shows the effectiveness of the proposed method for cancer disease diagnosis.

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Section: Proposed Methods Of Cancer Diagnosismentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Cancer Diagnosis of Microscopic Biopsy Images Using a Social Spider Optimisation-Tuned Neural Network

Balaji

Chidambaram

2021

Diagnostics

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“…In the pre-screening stage of a patient, binary class classification is required for physicians and radiologists, where the physicians take further action based on binary class classification. Preethi and Aishwarya [ 38 ] proposed a model to classify the brain tumor based on multiple stages. They combined the wavelet-based gray-level co-occurrence matrix and GLCM to produce the feature matrix.…”

Section: Related Workmentioning

confidence: 99%

MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers

Kang

Ullah

Gwak

2021

Sensors

346

140

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Brain tumor classification plays an important role in clinical diagnosis and effective treatment. In this work, we propose a method for brain tumor classification using an ensemble of deep features and machine learning classifiers. In our proposed framework, we adopt the concept of transfer learning and uses several pre-trained deep convolutional neural networks to extract deep features from brain magnetic resonance (MR) images. The extracted deep features are then evaluated by several machine learning classifiers. The top three deep features which perform well on several machine learning classifiers are selected and concatenated as an ensemble of deep features which is then fed into several machine learning classifiers to predict the final output. To evaluate the different kinds of pre-trained models as a deep feature extractor, machine learning classifiers, and the effectiveness of an ensemble of deep feature for brain tumor classification, we use three different brain magnetic resonance imaging (MRI) datasets that are openly accessible from the web. Experimental results demonstrate that an ensemble of deep features can help improving performance significantly, and in most cases, support vector machine (SVM) with radial basis function (RBF) kernel outperforms other machine learning classifiers, especially for large datasets.

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“…In the study conducted by Preethi et al [22], wavelet texture features and deep neural networks (DNNs) were utilized for brain tumor classification. The feature matrix was generated by combining wavelet texture features (GLCM) and wavelet GLCM.…”

Section: Related Workmentioning

confidence: 99%

“…In the first set, an 8-channel conv layer with a filter size of 3 × 3 × 1, stride [11], and padding 'same' is employed. The BN layer utilizes the 8 channels, followed by ReLU and max-pooling layers with a size of 2 × 2, stride [22], and padding [0000]. Similarly, in the second set, a 16-channel conv layer with a filter size of 3×3×8, stride [11], and padding 'same' is used, along with BN, ReLU, and max-pooling layers with the same configurations as in the first set.…”

Section: Layers and Their Propertiesmentioning

confidence: 99%

EMU-Net: Automatic Brain Tumor Segmentation and Classification Using Efficient Modified U-Net

Aly,

Alotaibi

2023

CMC

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Tumor segmentation is a valuable tool for gaining insights into tumors and improving treatment outcomes. Manual segmentation is crucial but time-consuming. Deep learning methods have emerged as key players in automating brain tumor segmentation. In this paper, we propose an efficient modified U-Net architecture, called EMU-Net, which is applied to the BraTS 2020 dataset. Our approach is organized into two distinct phases: classification and segmentation. In this study, our proposed approach encompasses the utilization of the gray-level co-occurrence matrix (GLCM) as the feature extraction algorithm, convolutional neural networks (CNNs) as the classification algorithm, and the chi-square method for feature selection. Through simulation results, the chi-square method for feature selection successfully identifies and selects four GLCM features. By utilizing the modified U-Net architecture, we achieve precise segmentation of tumor images into three distinct regions: the whole tumor (WT), tumor core (TC), and enhanced tumor (ET). The proposed method consists of two important elements: an encoder component responsible for down-sampling and a decoder component responsible for up-sampling. These components are based on a modified U-Net architecture and are connected by a bridge section. Our proposed CNN architecture achieves superior classification accuracy compared to existing methods, reaching up to 99.65%. Additionally, our suggested technique yields impressive Dice scores of 0.8927, 0.9405, and 0.8487 for the tumor core, whole tumor, and enhanced tumor, respectively. Ultimately, the method presented demonstrates a higher level of trustworthiness and accuracy compared to existing methods. The promising accuracy of the EMU-Net study encourages further testing and evaluation in terms of extrapolation and generalization.

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Combining Wavelet Texture Features and Deep Neural Network for Tumor Detection and Segmentation Over MRI

Cited by 12 publications

References 15 publications

Cancer Diagnosis of Microscopic Biopsy Images Using a Social Spider Optimisation-Tuned Neural Network

Cancer Diagnosis of Microscopic Biopsy Images Using a Social Spider Optimisation-Tuned Neural Network

MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers

EMU-Net: Automatic Brain Tumor Segmentation and Classification Using Efficient Modified U-Net

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