X-Cluster, A Novel and Efficient Approach towards Unsupervised Learning

Sharma, Mugdha

doi:10.1109/icmira.2013.56

Cited by 3 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then the author combined the method with auto-encoder and extends it to the vision field. Shaham et al (2016) first demonstrated that some crowdsourcing algorithms can be replaced by a Restricted Boltzmann Machine with a single hidden neuron, then propose an RBM-based Deep Neural Net (DNN) used for unsupervised ensemble learning. The unsupervised ensemble method also makes some contribution to the field of Natural Language Processing.…”

Section: Consensusmentioning

confidence: 99%

Ensemble deep learning: A review

Ganaie

Tanveer

et al. 2022

Engineering Applications of Artificial Intelligence

943

233

View full text Add to dashboard Cite

Section: Consensusmentioning

confidence: 99%

Ensemble deep learning: A review

Ganaie

Tanveer

et al. 2022

Engineering Applications of Artificial Intelligence

943

233

View full text Add to dashboard Cite

“…DBN typically learns in two stages, the first of which is unsupervised pretraining using unlabeled instances and the second of which is supervised fine-tuning using labeled instances. Although, such a training procedure could lead to two issues: First, because of coadaptation [248], multiple hidden units may have a tendency to behave in similar fashion [249], and second, because of the sparseness and specificity of activated neurons, a few of which may always be dead or interacting [250]. In order to address these two issues, Zhong et al [251] regularized the pretraining and fine-tuning operation by enforcing a diversification before improving the DBN's classification performance for HSI.…”

Section: Deep Belief Networkmentioning

confidence: 99%

Conventional to Deep Ensemble Methods for Hyperspectral Image Classification: A Comprehensive Survey

Ullah,

Khan

et al. 2024

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

View full text Add to dashboard Cite

Hyperspectral image classification has become a hot research topic. HSI has been widely used in a wide range of realworld application areas due to the in-depth spectral information stored within each pixel. Noticeably, the detailed features -i.e., a nonlinear correlation between the obtained spectral data and the correlating HSI data object, generate efficient classification results that are complex for traditional techniques. Deep Learning (DL) has recently been validated as an influential feature extractor that efficiently identifies the nonlinear issues that have arisen in various computer vision challenges. This motivates using DL for Hyperspectral Image Classification (HSIC), which shows promising results. This survey provides a brief description of DL for HSIC and compares cutting-edge methodologies in the field. We will first summarize the key challenges for HSIC, and then we will discuss the superiority of DL and DL-ensemble in addressing these issues. In this article, we divide the state-of-the-art DL methodologies and DL with ensemble into spectral features, spatial features, and combined spatial-spectral features in order to comprehensively and critically evaluate the progress (future research directions as well) of such methodologies for HSIC. Furthermore, we will take into account that DL involves a substantial percentage of labeled training images, whereas obtaining such a number for HSI is time and costconsuming. As a result, this survey describes some methodologies for improving the classification performance of DL techniques, which can serve as future recommendations.

show abstract

“…In [14], Discrete step algorithm for X-Ray bone image segmentation. Reference [15], Dataset generated and collected locally 3D MRI Scans, Artificial neural network (ANN) was employed to learn the mapping function between the CDI feature space and OA severity, With 70.6% accuracy.…”

Section: Literature Surveymentioning

confidence: 99%

Osteoarthritis Detection Algorithm Using CNN and Image Processing Techniques

Ali

2024

Journal of Communication Sciences and Information Technology

View full text Add to dashboard Cite

This paper presents a fully developed computer aided diagnosis (CAD) system for early knee Osteoarthritis (OA) detection using knee medical imaging and machine learning algorithms. The medical images are first preprocessed in the Fourier domain using a circular Fourier filter. Osteoarthritis (OA) is a chronic disease prevalent worldwide. There are two primary forms of osteoarthritis, affecting mainly the fingers, thumbs, spine, hips, knees, and big-toes, while secondary occurs with pre-existing joint anomalies. OA most commonly occurs in older individuals, but it can occur in adults of any age. OA is also known as degenerative joint disease, degenerative arthritis, and arthritis with wear and tear. Diagnosis of such disease is normally rendered by examining the joint scan of the Image with X-ray of knee. MRI analyzes are conducted by well-trained radiologists and orthopedists. The other side of this study is that it requires time and can be subject to loss of precision. The manual segmentation of images from a great number of scan images is a tedious and time-consuming procedure. Automatic segmentation and interpretation of joint MRI scans is therefore needed to increase the precision of clinical outcomes and bone calculation. In recent years, the advent of deep-learning technologies within medical systems is causing a transition. This can process a large volume of data to have greater precision. Deep learning methods can thus be used effectively for the automatic segmentation of MRI scans. This paper offers an overview of the different models and their output using deep learning and image processing techniques. Diagnosis of the illness is conferred from the image data. The state-of-the-art analysis is then discussed on Convolutional Neural Network (CNN) and Image Recognition. Finally, a comparative overview of the proposed model with other state-of-the-art techniques are presented based on the performance metrics. In the proposed detection algorithm, we found that all the suggested models achieved a higher level of predictive accuracy, greater than 90%, in detecting osteoarthritis. However, the best-performing model was the pretrained VGG-16 model with edge detection, which achieved a training accuracy of 99% and a testing accuracy of 92%.

show abstract

X-Cluster, A Novel and Efficient Approach towards Unsupervised Learning

Cited by 3 publications

References 14 publications

Ensemble deep learning: A review

Ensemble deep learning: A review

Conventional to Deep Ensemble Methods for Hyperspectral Image Classification: A Comprehensive Survey

Osteoarthritis Detection Algorithm Using CNN and Image Processing Techniques

Contact Info

Product

Resources

About