Skin Disease Recognition Method Based on Image Color and Texture Features

Wei, Lisheng; Gan, Quan; Ji, Tao

doi:10.1155/2018/8145713

Cited by 102 publications

(35 citation statements)

References 7 publications

(10 reference statements)

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“…The classification accuracy of each class is separately calculated 85%, 90%, 95%, respectively. On average, the correct accuracy of classification can be considered 90% [17]. In this article, using the SVM method, the correct classification rate was first calculated 73%, and after applying genetic coefficients, it was calculated 99%.…”

Section: Resultsmentioning

confidence: 99%

Applying Different Pattern Recognition Methods for Identifying Skin Diseases

Zirjam¹,

Rajebi²

2020

MLR

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Section: Resultsmentioning

confidence: 99%

Applying Different Pattern Recognition Methods for Identifying Skin Diseases

Zirjam¹,

Rajebi²

2020

MLR

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“…Skin diseases are also classified through the necessary image processing approaches like morphological operations for skin detection [ 22 , 23 ]. Morphological opening, closing, dilation, and erosion mostly rely on the binary image generated through the thresholding, and resultantly at most care must be taken to determine the optimal threshold value.…”

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

451

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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“…The study was performed with a view to future integration of matters concerning skin diseases in children. In [16][20], Three forms of skin diseases such as herpes, dermatitis, and psoriasis are recognized through a modern recognition system. Skin images have been preprocessed by filtering and modifying to strip out noise and irrelevant characteristics.…”

Section: Related Workmentioning

confidence: 99%

Detection of Skin Disease in Pervasive Health Care Using Convolution Neural Network

Sarojini¹,

Simon²,

Moorthy³

et al. 2021

Proceedings of the Fist International Conference on Advanced Scientific Innovation in Science, Engineering and Technology, ICA

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Abstrac.This paper is about detecting skin disease using convolutional neural network in pervasive health care. Various optimizers in keras model are compared and the optimizer with the highest accuracy percentage is employed to predict skin disease through abnormalities in skin images. Detecting skin diseases by viewing images of skin can be an advancement to a great extent. There are many algorithms in machine language which would help to serve the above mentioned scenario. One of the most efficient algorithms that is being used here is convolutional neural networks. It simplifies the input image by reducing its dimensionality which makes prediction of patterns much easier.Convolutional neural network is used along with keras using tensorflow as backend which enables higher efficiency and accuracy.

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Skin Disease Recognition Method Based on Image Color and Texture Features

Cited by 102 publications

References 7 publications

Applying Different Pattern Recognition Methods for Identifying Skin Diseases

Applying Different Pattern Recognition Methods for Identifying Skin Diseases

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

Detection of Skin Disease in Pervasive Health Care Using Convolution Neural Network

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