Dermatological disease diagnosis using color-skin images

Arifin, M. Shamsul; Kibria, Muhammad Golam; Firoze, Adnan; Amini, Morteza; Yan, Hong

doi:10.1109/icmlc.2012.6359626

Cited by 64 publications

(21 citation statements)

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“…In the same reference, both unsupervised and supervised clustering is tested achieving an accuracy of 86% and 92% respectively. In the approaches where neural networks are employed, the number of nodes in the input, hidden and output layers are indicatively 16-8-6 in [34], 34-16-8-1 in [38] while in [33] the number of hidden nodes tested were between 70 and 150. A large number of input samples are used in all the approaches of Table 6, for training.…”

Section: Experimental Results Presented In the Referenced Approachesmentioning

confidence: 99%

“…Convolutional ANNs are employed in [49,50] for retinal vessel characterization, in [41] for detecting mammographic masses. A feed forward back propagation neural network (FFBP-NN) is used in [21,33] for dermatological disease diagnosis. ANNs have also been used in other skin disorder classification applications: [34,36,38].…”

Section: Classification Methodsmentioning

confidence: 99%

“…The poor classification accuracy is explained by the fact that only two features were used. The k-means clustering algorithm has been employed in [33,38] for multiple skin disease classification (e.g., psoriasis, dermatitis, lichen planus, pityriasis, etc. ), in [31] for acne detection, in the review of machine learning methods for biotic stress detection in precision crop protection [55], etc.…”

Section: Classification Methodsmentioning

confidence: 99%

“…Other skin disorders can be detected by image processing applications that verify the existence of a single disease e.g., psoriasis in [29] and acne in [30,31]. Other applications discriminate between multiple skin diseases [32][33][34][35][36][37][38]. The image processing in most of these cases is performed in RGB although different color spaces have also been employed like hue saturation value (HSV), YCbCr [30].…”

Section: Introductionmentioning

confidence: 99%

“…Smart phone applications are also available for skin disorder diagnosis like those described in [31,39,40]. The dataset sources in these references are Atlas of Clinical Dermatology (Churchill Livingstone, 2002), eCureMe Online Medical Dictionary [33], UCI Repository of Machine learning databases [34,38], Dermnet Skin Disease Atlas [36], in the references of [37].…”

Section: Introductionmentioning

confidence: 99%

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A Review of Image Processing Techniques Common in Human and Plant Disease Diagnosis

Petrellis

2018

Symmetry

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Image processing has been extensively used in various (human, animal, plant) disease diagnosis approaches, assisting experts to select the right treatment. It has been applied to both images captured from cameras of visible light and from equipment that captures information in invisible wavelengths (magnetic/ultrasonic sensors, microscopes, etc.). In most of the referenced diagnosis applications, the image is enhanced by various filtering methods and segmentation follows isolating the regions of interest. Classification of the input image is performed at the final stage. The disease diagnosis approaches based on these steps and the common methods are described. The features extracted from a plant/skin disease diagnosis framework developed by the author are used here to demonstrate various techniques adopted in the literature. The various metrics along with the available experimental conditions and results presented in the referenced approaches are also discussed. The accuracy achieved in the diagnosis methods that are based on image processing is often higher than 90%. The motivation for this review is to highlight the most common and efficient methods that have been employed in various disease diagnosis approaches and suggest how they can be used in similar or different applications.

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Section: Experimental Results Presented In the Referenced Approachesmentioning

confidence: 99%