RETRACTED: Auto encoder based dimensionality reduction and classification using convolutional neural networks for hyperspectral images

Ramamurthy, Madhumitha; Robinson, Y. Harold; Vimal, S.; Suresh, A.

doi:10.1016/j.micpro.2020.103280

Cited by 85 publications

(35 citation statements)

References 25 publications

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“…Convolutional neural networks (CNN), also referred to as covnets, are neural networks that may have common parameters. A covnets consists of a series of layers, such that each layer is capable of transforming one volume to another through differentiable function [ 32 , 33 ]. There are various types of layers involved in CNN.…”

Section: Methodsmentioning

confidence: 99%

A novel LSTM–CNN–grid search-based deep neural network for sentiment analysis

2021

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Section: Methodsmentioning

confidence: 99%

A novel LSTM–CNN–grid search-based deep neural network for sentiment analysis

2021

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“…Similarly, Paoletti et al [7] analyzed the impact of the input spatial size on model accuracy. Ramamurthy et al [8] conducted image denoising and dimensionality reduction by combining autoencoders and CNNs. Also, Zhao et al explored 2D CNNs [9] to extract spatial information from reduced HSI data using principal component analysis (PCA) for classification, but failed to exploit the entire range of spectral information contained in the HSI.…”

Section: Introductionmentioning

confidence: 99%

Morphological Convolutional Neural Networks for Hyperspectral Image Classification

Roy¹,

Mondal

Paoletti

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Convolutional neural networks (CNNs) have become quite popular for solving many different tasks in remote sensing data processing. The convolution is a linear operation which extracts features from the input data. However, nonlinear operations are able to better characterize the internal relationships and hidden patterns within complex remote sensing data, such as hyperspectral images (HSIs). Morphological operations are powerful nonlinear transformations for feature extraction that preserve the essential characteristics of the image, such as borders, shape and structural information. In this paper, a new end-to-end morphological deep learning framework (called MorphConvHyperNet) is introduced. The proposed approach efficiently models nonlinear information during the training process of HSI classification. Specifically our method includes spectral and spatial morphological blocks to extract relevant features from the HSI input data. These morphological blocks consist of two basic 2D morphological operators (erosion and dilation) in the respective layers, followed by a weighted combination of the feature maps. Both layers can successfully encode the nonlinear information related to shape and size, playing an important role in classification performance. Our experimental results, obtained on five widely used HSIs, reveal that our newly proposed MorphConvHyperNet offers comparable (and even superior) performance when compared to traditional 2D and 3D

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“…(Please refer to Tables 1 and 2). PCA [70], AE [71], and DWT [72,73] techniques are employed for the integration process as they are popular feature reduction methods that have been extensively used in the machine learning literature. SVM performs well with large dimension space and as it uses kernel function which maps the feature space into a new domain that can easily separate between classes of a dataset.…”

Section: Discussionmentioning

confidence: 99%

DIAROP: Automated Deep Learning-Based Diagnostic Tool for Retinopathy of Prematurity

Attallah

2021

Diagnostics

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Retinopathy of Prematurity (ROP) affects preterm neonates and could cause blindness. Deep Learning (DL) can assist ophthalmologists in the diagnosis of ROP. This paper proposes an automated and reliable diagnostic tool based on DL techniques called DIAROP to support the ophthalmologic diagnosis of ROP. It extracts significant features by first obtaining spatial features from the four Convolution Neural Networks (CNNs) DL techniques using transfer learning and then applying Fast Walsh Hadamard Transform (FWHT) to integrate these features. Moreover, DIAROP explores the best-integrated features extracted from the CNNs that influence its diagnostic capability. The results of DIAROP indicate that DIAROP achieved an accuracy of 93.2% and an area under receiving operating characteristic curve (AUC) of 0.98. Furthermore, DIAROP performance is compared with recent ROP diagnostic tools. Its promising performance shows that DIAROP may assist the ophthalmologic diagnosis of ROP.

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RETRACTED: Auto encoder based dimensionality reduction and classification using convolutional neural networks for hyperspectral images

Cited by 85 publications

References 25 publications

A novel LSTM–CNN–grid search-based deep neural network for sentiment analysis

A novel LSTM–CNN–grid search-based deep neural network for sentiment analysis

Morphological Convolutional Neural Networks for Hyperspectral Image Classification

DIAROP: Automated Deep Learning-Based Diagnostic Tool for Retinopathy of Prematurity

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