Fusion of Contourlet Transform and Zernike Moments using Content based Image Retrieval for MRI Brain Tumor Images

Pandian, A. Anbarasa; Balasubramanian, R.

doi:10.17485/ijst/2016/v9i29/93837

Cited by 8 publications

(4 citation statements)

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“…Poonguzhali et al [ 15 ] in 2019 analyzed 20 patient images using RCNN and SVM classifiers and achieved a sensitivity of 82% and specificity of 99%. Pandian et al [ 16 ] in 2017 analyzed 1000 images using Convnet techniques and attained an accuracy of 97%. Joshi et al [ 17 ] in 2019 used a CNN technique for image analysis and achieved an accuracy of 79.07%.…”

Section: Related Workmentioning

confidence: 99%

“…Poonguzhali et al [ 15 ] used a RCNN and SVM classifier on 20 patient images and achieved a sensitivity of 82% and specificity of 99%. Pandian et al [ 16 ] used convnet, slicenet, and VGNet on 1000 images and achieved an accuracy of 97%. Joshi et al [ 17 ] used a CNN and achieved an accuracy of 79.07%.…”

Section: Proposed Weighted Average Ensemble Deep Learning Model Archi...mentioning

confidence: 99%

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Weighted Average Ensemble Deep Learning Model for Stratification of Brain Tumor in MRI Images

Anand

Gupta

et al. 2023

Diagnostics

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Brain tumor diagnosis at an early stage can improve the chances of successful treatment and better patient outcomes. In the biomedical industry, non-invasive diagnostic procedures, such as magnetic resonance imaging (MRI), can be used to diagnose brain tumors. Deep learning, a type of artificial intelligence, can analyze MRI images in a matter of seconds, reducing the time it takes for diagnosis and potentially improving patient outcomes. Furthermore, an ensemble model can help increase the accuracy of classification by combining the strengths of multiple models and compensating for their individual weaknesses. Therefore, in this research, a weighted average ensemble deep learning model is proposed for the classification of brain tumors. For the weighted ensemble classification model, three different feature spaces are taken from the transfer learning VGG19 model, Convolution Neural Network (CNN) model without augmentation, and CNN model with augmentation. These three feature spaces are ensembled with the best combination of weights, i.e., weight1, weight2, and weight3 by using grid search. The dataset used for simulation is taken from The Cancer Genome Atlas (TCGA), having a lower-grade glioma collection with 3929 MRI images of 110 patients. The ensemble model helps reduce overfitting by combining multiple models that have learned different aspects of the data. The proposed ensemble model outperforms the three individual models for detecting brain tumors in terms of accuracy, precision, and F1-score. Therefore, the proposed model can act as a second opinion tool for radiologists to diagnose the tumor from MRI images of the brain.

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Section: Related Workmentioning

confidence: 99%

Section: Proposed Weighted Average Ensemble Deep Learning Model Archi...mentioning

confidence: 99%

Weighted Average Ensemble Deep Learning Model for Stratification of Brain Tumor in MRI Images

Anand

Gupta

et al. 2023

Diagnostics

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“…In (32) presented the DNN and Extreme Learning Machine (ELM) based brain image classification using prominent features. The contourlet transform and Zernike moments were used to extract the texture and shape features respectively.…”

Section: Literature Surveymentioning

confidence: 99%

An Efficient Medical Image Retrieval and Classification using Deep Neural Network

Chethan¹

2020

IJST

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Background/Objectives: The main objective of this work is to obtain an efficient brain tumor image retrieval and classification using Deep Neural Network (DNN). Methods/Statistical analysis: The features from the medical images are extracted by using tamura feature extraction, Local Ternary Pattern (LTP) and Histogram of Oriented Gradients (HOG). Subsequently, an Infinite Feature Selection (Inf-FS) technique is incorporated to select optimum features from feature vector, which leads to improve the classification process using sparse auto encoder based DNN. Furthermore, the retrieval performance of the proposed method is improved by Euclidean Distance technique. Findings: An Open Access Series of Imaging Studies (OASIS) and Contrast Enhanced- Magnetic Resonance Image (CE-MRI) datasets are utilized to analyze the proposed method. The sparse auto encoder based DNN classification scheme yields an overall accuracy of 95.34% in OASIS dataset and 99.87% in CEMRI dataset with improved sensitivity, specificity, error rate. The retrieval performance of proposed technique is assessed in terms of Average Retrieval Precision (ARP) and compared with two existing methods such as Local Mesh Vector Co-occurrence Pattern (LMVCoP) and Content Based Image Retrieval- Convolutional Neural Network (CBIR-CNN). The ARP of the proposed method for CE-MRI and OASIS dataset is 98.33% and 88.25% that is high when compared to the CBIR-CNN, LMVCoP method. Novelty/Applications: An appropriate feature selection using Inf-FS and DNN based nonlinear feature data classification are used in the applications of medical image retrieval.

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“…The effectiveness of several distance metrics, including Euclidean, Chebyshev, Cityblock, Canberra and Standardized Euclidean (Seuclidean), is assessed in this article on a medical database with six classes, each of which contains 100 images. The research confirms that the suggested approach successfully retrieves images from the database, and the outcomes are evaluated against those of alternative approaches using accuracy and recall criteria.Pandian et al[60] discovered a texture fusion strategy for T1 and T2 weighted MRI scans. The method involves extracting texture and shape features from brain tumor images, selecting features using Genetic Algorithm and Particle Swarm Optimization (PSO), and classifying brain tumors using Deep Neural Network and Extreme Learning Machine (ELM).…”

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confidence: 99%

Fusion of Textural and Visual Information for Medical Image Modality Retrieval Using Deep Learning-Based Feature Engineering

Iqbal,

Qureshi,

Alhussein

et al. 2023

IEEE Access

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Medical image retrieval is essential to modern medical treatment because it enables doctors to diagnose and treat a variety of illnesses. In this study, we present an innovative technique for selecting the methodology of medical images by combining textural and visual information. Knowing the imaging process behind an idea, such as a chest X-ray, skin dermatology, or breast histopathology image, may be extremely helpful to healthcare professionals since it can aid in image investigation and provide important information about the imaging technique used. We use deep learning-based feature engineering to do this, using both the textural and visual components of healthcare images. We extract detailed visual information from the images using a predefined Convolutional Neural Network (CNN). The Global-Local Pyramid Pattern (GLPP), Zernike moments, and Haralick are also used to physically separate the pertinent parts from the images' other visual and factual aspects. These essential characteristics, such as image modality and imaging technique-specific characteristics, provide additional information about the technology. We employ a feature fusion method that incorporates the depictions obtained from the two modalities in order to combine the textural and visual elements. This fusion process, which improves the discrimination capacity of the feature vectors, makes accurate modality classification possible. We conducted trials on a sizable dataset consisting of various medical images to assess the effectiveness of our proposed method. The results indicate that, in comparison to conventional methods, our technique outperforms modality retrieval, with a precision of 95.89 and a recall of 96.31. The accuracy and robustness of the classification task are greatly creased by the combination of textural and visual data. Through the integration of textural and visual information, our work offers a unique method for recovering the modality of medical images. This method has the potential to greatly improve the speed and accuracy of medical image processing and diagnosis by helping experts rapidly and accurately identify the imaging technology being utilized.

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Fusion of Contourlet Transform and Zernike Moments using Content based Image Retrieval for MRI Brain Tumor Images

Cited by 8 publications

References 4 publications

Weighted Average Ensemble Deep Learning Model for Stratification of Brain Tumor in MRI Images

Weighted Average Ensemble Deep Learning Model for Stratification of Brain Tumor in MRI Images

An Efficient Medical Image Retrieval and Classification using Deep Neural Network

Fusion of Textural and Visual Information for Medical Image Modality Retrieval Using Deep Learning-Based Feature Engineering

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