Interpretable features fusion with precision MRI images deep hashing for brain tumor detection

Özbay, Erdal; Özbay, Feyza Altunbey

doi:10.1016/j.cmpb.2023.107387

Cited by 19 publications

(5 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One of the options, including support vector machines and artificial neural networks, might be chosen as machine learning. [3] Within the parameters of the study, brain tumors were segmented using transfer learning and a convolutional neural network. Feedforward training of CNNs begins with the first input layer and continues to the final classification layer; following that, error back-propagation begins with the final classification layer and moves forward to the first convolutional layer.…”

Section: Convolutional Neural Network (Cnn) For Tumor Classification ...mentioning

confidence: 99%

“…In terms of efficiency metrics, GoogLeNet performed better than AlexNet, where GoogLeNet's work on classifying brain anomalies used deep transfer learning and obtained impressive classification performance [2]. Wavelet transform (WT) [3] is an important strategy for feature extraction from MR brain images, but it necessitates a substantial amount of storage and is computationally expensive. Wavelet transform (WT) allows analysis of images at various levels of resolution due to its multiple-resolution analytic property [4].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improving Brain Tumor Segmentation in MRI Images through Enhanced Convolutional Neural Networks

Ayomide¹,

Aris²,

Zolkepli³

2023

IJACSA

View full text Add to dashboard Cite

Achieving precise tumor segmentation is essential for accurate diagnosis. Since brain tumors segmentation require a significant training process, reducing the training time is critical for timely treatment. The research focuses on enhancing brain tumor segmentation in MRI images by using Convolutional Neural Networks and reducing training time by using MATLAB's GoogLeNet, anisotropic diffusion filtering, morphological operation, and sector vector machine for MRI images. The proposed method will allow for efficient analysis and management of enormous amounts of MRI image data, the earliest practicable early diagnosis, and assistance in the classification of normal, benign, or malignant patient cases. The SVM Classifier is used to find a cluster of tumors development in an MR slice, identify tumor cells, and assess the size of the tumor that appears to be present in order to diagnose brain tumors. The proposed method is evaluated using a dataset from Figshare that includes coronal, sagittal, and axial views of images taken with a T1-CE MRI modality. The accuracy of 2D tumor detection and segmentation are increased, enabling more 3D detection, and achieving a mean classification accuracy of 98% across system records. Finally, a hybrid approach of GoogLeNet deep learning algorithm and Convolution Neural Network-Support Vector Machines (CNN-SVM) deep learning is performed to increase the accuracy of tumor classification. The evaluations show that the proposed technique is significantly more effective than those currently in use. In the future, enhancement of the segmentation using artificial neural networks will help in the earlier and more precise detection of brain tumors. Early detection of brain tumors can benefit patients, healthcare providers, and the healthcare system as a whole. It can reduce healthcare costs associated with treating advanced stage tumors, and enables researchers to better understand the disease and develop more effective treatments.

show abstract

Section: Convolutional Neural Network (Cnn) For Tumor Classification ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving Brain Tumor Segmentation in MRI Images through Enhanced Convolutional Neural Networks

Ayomide¹,

Aris²,

Zolkepli³

2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…Using CNN and LSTM networks together has a great impact in achieving a higher accuracy value in the proposed model. CNN and LSTM networks are frequently used in the literature [21][22][23].…”

Section: Comparison Of All Modelsmentioning

confidence: 99%

Automated Grading of Glioma Using Deep Neural Networks

YILDIRIM,

ASLAN,

CENGİL

et al. 2023

NATURENGS MTU Journal of Engineering and Natural Sciences Malatya Turgut Ozal University

View full text Add to dashboard Cite

Gliomas are one of the most common tumors in the brain. It is possible to grade gliomas as Lower-Grade Glioma (LGG) and Glioblastoma Multiforme (GBM). Clinical and molecular/mutation factors come to the fore in the grading of gliomas. Molecular tests used to grade glioma are expensive and time consuming. In this study, deep learning networks were used for glioma grading. Long short-term memory (LSTM) and Convolutional neural network (CNN) were used together in the proposed model. The developed model was also compared with 6 different classifiers accepted in the literature. Among the models used in the study, the developed model achieved the highest performance. In this study, glioma grading was performed for the purpose of improving performance and reducing costs.

show abstract

“…In the context of medical images, features could encompass textural patterns, shapes, or intensity gradients. Convolutional Neural Networks play a pivotal role in this process by automatically learning relevant features from images [7]. This translates raw pixel data into comprehensible information that forms the foundation for subsequent classification algorithms.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Brain Tumor Recognition with Ensemble support Vector-based Local Coati Algorithm and CNN Feature Extraction

Sumithra,

et al. 2024

Preprint

View full text Add to dashboard Cite

Nowadays, brain tumor (BT) recognition has become a common phenomenon in the healthcare industry. In the medical system,BT identification and classification can take a significant part in the diagnostics and considerations of the patients. BT is characterized as an abnormal mass of tissue in which the cells proliferate unexpectedly with no control over cell proliferation. In recent years, improvements in machine learning (ML), particularly deep learning (DL) procedures, have shown significant potential for mechanizing and improving these undertakings by utilizing medical imaging information. Also, we examine the difficulties and probabilities in this field, including information shortage, model interpretability, and moral contemplations. To overcome these challenges Ensemble support Vector-based Local Coati (ESV-LC) Algorithm is employed to identify and classify the brain tumor disease in the patients. For optimal classification, the features need to be extracted and this can be achieved by employing the Convolutional Neural network (CNN). To accurately classify BT, Ensemble Support Vector Machine (ESVM) is involved, which enhances classification performance, and hyperparameter tuning is performed through Local Search Coati Optimization. The Brain Tumor Image Dataset and Figshare Brain Tumor dataset are utilized for BT classification and identification. The performance metrics like Accuracy, Precision, Sensitivity, Specificity, and F1-score are to be evaluated, where the accuracy achieves the value of 98.3%, sensitivity of 97.6%, precision of 97.7%, specificity of 98.1%, and F1-score of 96.7% respectively.

show abstract

Interpretable features fusion with precision MRI images deep hashing for brain tumor detection

Cited by 19 publications

References 30 publications

Improving Brain Tumor Segmentation in MRI Images through Enhanced Convolutional Neural Networks

Improving Brain Tumor Segmentation in MRI Images through Enhanced Convolutional Neural Networks

Automated Grading of Glioma Using Deep Neural Networks

Optimizing Brain Tumor Recognition with Ensemble support Vector-based Local Coati Algorithm and CNN Feature Extraction

Contact Info

Product

Resources

About