A Model for Classification and Diagnosis of Skin Disease using Machine Learning and Image Processing Techniques

AlDera, Shaden Abdulaziz; Othman, Mohamed Tahar Ben

doi:10.14569/ijacsa.2022.0130531

Cited by 15 publications

(8 citation statements)

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“…The analyses of the works with the purpose of developing the methods for the treatment of psoriasis disease images in CAIR CAD systems were performed. The analyzed works can be conditionally divided into the following groups: development of the classification methods of psoriasis disease images [2,3], [4,5], [6,7]; development of segmentation methods of psoriasis disease images [8,9], [10,11]; development of the automatic classification methods of psoriasis disease images and evaluation of psoriasis disease images parameters [12,13], [14].…”

Section: Analysis Of Texture Image Segmentation Methods In Dermatolog...mentioning

confidence: 99%

“…Images of objects are constructing for classification using the methods proposed in [6,7] taking into account the features of the images. The method developed in [6] consists of the following steps: image acquisition; pre-processing using median filtering; segmentation; identification; classification. At the identification stage, texture features are obtained using Gabor and Sobel filters and Entropy calculation.…”

Section: Analysis Of Texture Image Segmentation Methods In Dermatolog...mentioning

confidence: 99%

“…However, the image of psoriasis disease can be described by both textural and color features. To take into account texture and color features in the segmentation of psoriasis images, classification methods are used [2,3], [4,5], [6,7], which provide high quality segmentation with low efficiency. The use of mobile CAD systems for express diagnostics requires the development of segmentation methods that would improve the quality of segmentation with sufficient efficiency for the needs of practice.…”

Section: Introductionmentioning

confidence: 99%

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Texture segmentation method for computer-assisted dermatologic diagnostic system

Volkova¹,

Ishchenko²

2022

HAIT

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The wide spread of dermatological diseases is an important medical and social problem. Doctors note the constant growth of psoriasis among people of all ages. The psoriasis disease symptoms are similar to the symptoms of such diseases as eczema, atopic dermatitis and medicinal disease. Therefore, there is a high probability of an error in the disease diagnosis, which prevents the full treatment and prevention of the disease. Dermatological diagnostic systems are decision-making support systems for dermatologists when establishing a diagnosis and assessing the severity of the disease course. The development of new image processing methods for dermatological diagnostic systems is an important task, which allow to increase the accuracy of the diagnostic decision. In this work, the segmentation method of psoriasis images for systems of medical dermatological diagnostics based on a vector-difference approach to improve the quality of segmentation was developed. The vector-difference approach allows to calculate a certain texture feature of the image as a vector transformation of various texture features by linear algebra methods. Psoriasis disease images can be described by texture (spectral, statistical, spectral-statistical) and color, so it is proposed to take into account textural and color characteristics of images for image segmentation. The color models that are most often used in segmentation methods of psoriasis disease images were analyzed. Based on the analysis, the Hue-Saturation-Intensity color model was chosen. It is proposed to use spectral, statistical and spectral-statistical texture models and color characteristics of image pixels to represent psoriasis disease images. The developed segmentation method includes the following stages: image pre-processing; identification; vector-difference transformation; threshold processing. At the pre-processing stage, homomorphic filtering was applied to psoriasis disease images. The result of the identification stage is a set of features calculated by the textural and color characteristics for image objects. The vector-difference transformation converts the texture image into intensity. Threshold processing is performed with a global threshold. Experimental research of the proposed segmentation method of psoriasis disease images was performed. As a result of the experimental research, it was found that the probability of correct identification of psoriasis disease area on average is 0.97, the probability of a false alarm is about 0.05

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Section: Analysis Of Texture Image Segmentation Methods In Dermatolog...mentioning

confidence: 99%

Section: Analysis Of Texture Image Segmentation Methods In Dermatolog...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Texture segmentation method for computer-assisted dermatologic diagnostic system

Volkova¹,

Ishchenko²

2022

HAIT

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“…Their framework has a 94.79% accuracy rate for identifying 8 skin infections. AlDera et al [4] displayed a skin malady conclusion that takes an influenced skin image and analyzes skin break out, psoriasis, melanoma, and cherry angioma. Upon that the dermnet NZ and map book dermatologico, they linked Otsu's strategy for picture division and the Gabor, Entropy, and Sobel methods for extracting features.…”

Section: Literature Reviewmentioning

confidence: 99%

Computer Vision Based Skin Disease Recognition

Saha

Majumder

2023

Preprint

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All over the world, various types of skin diseases are affecting an increasing number of people. It is incredibly simple to diagnose diseases with the aid of modern medical technology. However, there are times when people are unable to quickly travel to a hospital or diagnostic facility and it is also extremely expensive for most people, particularly in developing nations like Bangladesh. It is a good idea to have a backup plan in case something goes wrong, but it's not always possible. This is primarily an image processing method. Then total twelve features have been extracted from our all datasets consist of five diseases (Actinic keratosis, Benign, Eczema, Malignant, Psoriasis). Then, to learn our model, four different machine learning algorithms have utilized. For the psoriasis disease, the highest accuracy from the Random Forest classifier has obtained, which is 88%, and the F1-score is 96%. Therefore, this model can accurately recognize skin diseases with great effectiveness.

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“…Dermatologists often rely on their clinical expertise to diagnose these diseases, which can be time-consuming and subject to human error. Machine learning techniques, such as deep learning, have shown great promise in aiding the early detection and diagnosis of skin diseases like eczema and psoriasis [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Deep Learning Approach for Accurate Eczema and Psoriasis Skin Detection

Hammad,

Pławiak,

ElAffendi

et al. 2023

Sensors

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This study presents an enhanced deep learning approach for the accurate detection of eczema and psoriasis skin conditions. Eczema and psoriasis are significant public health concerns that profoundly impact individuals’ quality of life. Early detection and diagnosis play a crucial role in improving treatment outcomes and reducing healthcare costs. Leveraging the potential of deep learning techniques, our proposed model, named “Derma Care,” addresses challenges faced by previous methods, including limited datasets and the need for the simultaneous detection of multiple skin diseases. We extensively evaluated “Derma Care” using a large and diverse dataset of skin images. Our approach achieves remarkable results with an accuracy of 96.20%, precision of 96%, recall of 95.70%, and F1-score of 95.80%. These outcomes outperform existing state-of-the-art methods, underscoring the effectiveness of our novel deep learning approach. Furthermore, our model demonstrates the capability to detect multiple skin diseases simultaneously, enhancing the efficiency and accuracy of dermatological diagnosis. To facilitate practical usage, we present a user-friendly mobile phone application based on our model. The findings of this study hold significant implications for dermatological diagnosis and the early detection of skin diseases, contributing to improved healthcare outcomes for individuals affected by eczema and psoriasis.

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A Model for Classification and Diagnosis of Skin Disease using Machine Learning and Image Processing Techniques

Cited by 15 publications

References 0 publications

Texture segmentation method for computer-assisted dermatologic diagnostic system

Texture segmentation method for computer-assisted dermatologic diagnostic system

Computer Vision Based Skin Disease Recognition

Enhanced Deep Learning Approach for Accurate Eczema and Psoriasis Skin Detection

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