Analysis on Shape Image Retrieval Using DNN and ELM Classifiers for MRI Brain Tumor Images

Pandian, A. Anbarasa; Balasubramanian, R.

doi:10.5815/ijieeb.2016.04.08

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…They have shown that by using hybrid features some improvement in the retrieval accuracy can be obtained. A. Anbarasa Pandian and R. Balasubramanian [26] have applied the CBIR technique for the MRI brain tumor images dataset. They used a shape feature for this.…”

Section: Existing Work In Cbirmentioning

confidence: 99%

A Novel Technique for Image Retrieval based on Concatenated Features Extracted from Big Dataset Pre-Trained CNNs

Bhuma¹,

Ramanjaneyulu²

2023

IJIGSP

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Accessing semantically relevant data from a database is not only essential in commercial applications but also in medical imaging diagnosis. Representation of the query image by its features and subsequently the dataset are the key factors in Content Based Image Retrieval (CBIR). Texture, shape and color are commonly used features for this purpose. Features extracted from the pre-trained Convolutional Neural Networks (CNNs) are used to improve the performance of CBIR methods. In this work, we explore a recent state of the art big dataset pre-trained CNNs which are known as Big Transfer Networks. Features extracted from Big Transfer Network have higher discriminative power compared to features of many other pre-trained CNNs. The idea behind the proposed work is to demonstrate the effectiveness of using features of big transfer networks for image retrieval. Further, features extracted from big transfer networks are concatenated to improve the performance of the proposed method. Feature diversity supplemented with network diversity should ensure good discriminative power for image retrieval. This idea is supported by performing simulations on four datasets with varying sizes in terms of number of images and classes. As feature size increases with the concatenation, we applied a dimensionality reduction algorithm i.e., Principal Component Analysis. Several distance metrics are explored in this work. By properly choosing the pre-trained CNNs and distance metric, it is possible to achieve higher mean average precisions. ImageNet-21K pre-trained CNN and Instagram pre-trained CNN are chosen in this work. Further, a pre-trained network trained on Imagenet-21K dataset is superior compared to the networks trained on ImageNet-1K dataset as there are more classes and presence of wide variety of images. This is demonstrated by applying our algorithm on four datasets i.e., COREL-100, CALTECH-101, FLOWER-17 and COIL-100. Simulations are presented for various precisions (scopes), and distance metrics. Results are compared with the existing algorithms and superiority of the proposed method in terms of mean Average Precision is shown.

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Section: Existing Work In Cbirmentioning

confidence: 99%

A Novel Technique for Image Retrieval based on Concatenated Features Extracted from Big Dataset Pre-Trained CNNs

Bhuma¹,

Ramanjaneyulu²

2023

IJIGSP

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“…𝐻 † = (𝐻 𝑇 𝐻) −1 𝐻 𝑇 (8) Secara lebih ringkas, algoritma ELM dengan SLFN dapat dimodelkan dengan langkah sebagai berikut [20], [12], [21], [22]. Input : Data training (𝑥 𝑖 , 𝑦 𝑖 ) dengan 𝑥 𝑖 = [𝑥 𝑖1 , 𝑥 𝑖2 , … , 𝑥 𝑖𝑑 ] 𝑇 ∈ 𝑅 𝑑 dan 𝑦 𝑖 = [𝑦 𝑖1 , 𝑦 𝑖2 , … , 𝑦 𝑖𝑚 ] 𝑇 ∈ 𝑅 𝑚 .…”

Section: 𝐻𝛽 = 𝑌unclassified

Pemodelan Prediksi Harga Ethereum (Atribut: Open, High dan Low) dengan Algoritma Extreme Learning Machine

Kasliono,

Candraningrum,

Sari

2023

bits

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The price of cryptocurrencies such as Ethereum often experiences high fluctuations and is difficult to predict. This study aims to predict Ethereum prices using the Extreme Learning Machine (ELM) algorithm which is a fast and efficient machine learning method. Ethereum price data is collected from CoinMarketCap by scraping the data using CoinmarketCap Scraper from the cryptocmd library using Python. An ELM model is built by changing the number of hidden nodes to determine the optimal prediction model of Ethereum prices based on the smallest average MAPE. Model performance was evaluated using the mean absolute percentage error (MAPE) on the test data set. The results show that the ELM model built can predict Ethereum prices with an accuracy of 96.96%. The MAPE obtained is 3.035334%, with 9 hidden nodes in the ELM network architecture model that was built. This shows that the model can explain about 96.96% of the variation in Ethereum price data. Therefore, the ELM model can be used as an aid in making investment decisions

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“…In the study [19] the authors developed an extraction of shape features form MRI brain tumor image. The supervised learning algorithms like Deep Neural Network and Extreme Learning Machine are used to classify the brain tumor images.…”

Section: Related Workmentioning

confidence: 99%

Neural Network Modeling and Correlation Analysis of Brain Plasticity Mechanisms in Stroke Patients

I.V.¹,

Mayorova²,

V.V.³

et al. 2019

IJISA

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The aim of this research is the study of pathogenic signs, prognostically significant for the outcome of the disease and restoration of impaired functions at various stages of recovery after a stroke. This work describes a new method of applying a group of artificial neural network algorithms for each of the criteria and for each period of rehabilitation, and it is aimed at analyzing the structural and functional support of motor and higher cognitive functions, including speech and language as well as brain plasticity after ischemic stroke. The functional magnetic resonance imaging (fMRI, DTI) and clinical data machine learning algorithms were used. Self-organizing Kohonen and probabilistic neural network-based models with different structures and parameters were developed and applied for each criterion for periods of 3, 6, and 12 months of rehabilitation. For correlation analyses and modeling additional classifiers, we used: Decision Tree (DT), Support Vector Machine (SUM), k-Nearest Neighbor (KNN) clustering, and Logistic Regression (LR). In the performance evaluation, sensitivity, specificity, accuracy, error rate, and f-measure were used. The using of clinical parameters and mathematical modeling for analysis of brain plasticity mechanisms in stroke patients allowed in some cases to predict cognitive functions within the accuracy of 85-97%. Moreover, it is shown that the functional systems is represented by various brain structures, its synchronous activity and structural connectivity ensures the rapid and most complete restoration of motor and higher cognitive functions, including speech and language (effective post-stroke plasticity of the brain) after a course of neurorehabilitation.

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Analysis on Shape Image Retrieval Using DNN and ELM Classifiers for MRI Brain Tumor Images

Cited by 3 publications

References 17 publications

A Novel Technique for Image Retrieval based on Concatenated Features Extracted from Big Dataset Pre-Trained CNNs

A Novel Technique for Image Retrieval based on Concatenated Features Extracted from Big Dataset Pre-Trained CNNs

Pemodelan Prediksi Harga Ethereum (Atribut: Open, High dan Low) dengan Algoritma Extreme Learning Machine

Neural Network Modeling and Correlation Analysis of Brain Plasticity Mechanisms in Stroke Patients

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