Image classification based on quantum K-Nearest-Neighbor algorithm

Dang, Yijie; Jiang, Nan; Hu, Hao; Ji, Zhuoxiao; Zhang, Wenyin

doi:10.1007/s11128-018-2004-9

Cited by 102 publications

(40 citation statements)

References 42 publications

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“…K-Nearest Neighbor (KNN) [ 47 ] is the predominantly used classification model widely used in forecasting and predictive models. The models do not need training of the model.…”

Section: Related Workmentioning

confidence: 99%

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Srinivasu

SivaSai

Ijaz

et al. 2021

Sensors

445

169

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Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.

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“…K-Nearest Neighbor (KNN) [ 47 ] is the predominantly used classification model widely used in forecasting and predictive models. The models do not need training of the model.…”

Section: Related Workmentioning

confidence: 99%

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Srinivasu

SivaSai

Ijaz

et al. 2021

Sensors

445

169

View full text Add to dashboard Cite

show abstract

“…In their work, [12] proposed a k-Tree method to learn different optimal k values for different test and new samples, by involving a training stage in the kNN classification. In the training stage, kTree method first learns optimal k values for all training samples by a new sparse reconstruction model, and then constructed a decision tree using training samples and the learned optimal k values.…”

Section: Review Of Related Workmentioning

confidence: 99%

“…They [12] further proposed an improvement version of kTree method called k*Tree method to speed up the test stage by extra storing the information of the training.…”

Section: Review Of Related Workmentioning

confidence: 99%

“…The authors in [12] used the powerful parallel computing ability of quantum computers to optimize the efficiency of image classification. Their scheme was based on quantum K Nearest-Neighbor algorithm.…”

Section: Review Of Related Workmentioning

confidence: 99%

“…K-Neareast Neigbor (KNN) is a simple algorithm that can store all available cases and classifies new cases based on a similarity measure. K-Nearest Neighbor (KNN) algorithm is one of the distinctive methods used in image classification [12]. Nearest Neighbor classification of objects is based on their similarity to the training sets and the statistics defined.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Convolutional Neural Network with K-Neareast Neighbor for Image Classification

Abubakar¹,

Boukari²

2018

International Journal of Advanced Research in Computer and Comm

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Image classification forms the basis for computer vision which is a trending sub-field in Machine Learning. The Convolutional Neural Network (ConvNet) has recently achieved great success in many computer vision tasks. The common architecture of ConvNets contains many layers to recurrently extract suitable image features and feed them to the softmax function for classification which often displays low prediction performance. In this paper, we propose the use of K-Nearest Neighbor as classifier for the ConvNets and also introduce the use of Principal Component Analysis (PCA) for dimensionality reduction. When successfully implemented, the proposed system should be able to accurately classify images.

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