Psoriasis Skin Disease Identification Using Support Vector Machine(SVM) Image Classification and Determining the Growth Rate

Thamizhvani, T. R.; Ajayan, Aparna K; Sannidhya, V; Hemalatha, R.; Chandrasekaran, R

doi:10.1088/1742-6596/2318/1/012034

Cited by 5 publications

(5 citation statements)

References 7 publications

(5 reference statements)

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“…SVM modeling has proven effective in various applications and has recently gained momentum within healthcare and medicine. 59,60,61 This method has previously been employed for the detection of various diseases such as Diabetes, 62 Alzheimer's, 63,64 Psoriasis, 65 Hepatitis, 66 and more. 67,68 Utilizing this SVM ML model, we examined the ability of DNA-SWCNTs to identify differing cell phenotypes as a function of DNA length.…”

Section: Resultsmentioning

confidence: 99%

Machine Learning Assisted Spectral Fingerprinting for Immune Cell Phenotyping

Nadeem,

Lyons,

Kindopp

et al. 2024

Preprint

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Spectral fingerprinting has emerged as a powerful tool, adept at identifying chemical compounds and deciphering complex interactions within cells and engineered nanomaterials. Using near-infrared (NIR) fluorescence spectral fingerprinting coupled with machine learning techniques, we uncover complex interactions between DNA-functionalized single-walled carbon nanotubes (DNA-SWCNTs) and live macrophage cells, enabling in situ phenotype discrimination. Through the use of Raman microscopy, we showcase statistically higher DNA-SWCNT uptake and a significantly lower defect ratio in M1 macrophages as compared to M2 and naive phenotypes. NIR fluorescence data also indicate that distinctive intra-endosomal environments of these cell types give rise to significant differences in many optical features such as emission peak intensities, center wavelengths, and peak intensity ratios. Such features serve as distinctive markers for identifying different macrophage phenotypes. We further use a support vector machine (SVM) model trained on SWCNT fluorescence data to identify M1 and M2 macrophages, achieving an impressive accuracy of > 95%. Finally, we observe that the stability of DNA-SWCNT complexes, influenced by DNA sequence length, is a crucial consideration for applications such as cell phenotyping or mapping intra-endosomal microenvironments using AI techniques. Our findings suggest that shorter DNA-sequences like GT6 give rise to more improved model accuracy (> 87%) due to increased active interactions of SWCNTs with biomolecules in the endosomal microenvironment. Implications of this research extend to the development of nanomaterial-based platforms for cellular identification, holding promise for potential applications in real time monitoring of in vivo cellular differentiation.

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Section: Resultsmentioning

confidence: 99%

Machine Learning Assisted Spectral Fingerprinting for Immune Cell Phenotyping

Nadeem,

Lyons,

Kindopp

et al. 2024

Preprint

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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%

“…In the works of the second group, the authors develop segmentation methods for CAIR of medical CAD systems. The proposed methods, as well as the methods of the first group, use CNN algorithms [8,9], [10], in which images are fed to the network input and then the feature map is extracted from the input images by convolution operation [11].…”

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

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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“…The identification of water layers in paddy fields can be described as a "binary classification" problem. With the development of artificial intelligence, many researchers have used various machine-learning algorithms for classification tasks, including back propagation neural networks (BPNN) [29], random forests (RF) [30], support vector machines (SVM) [31,32], and K-nearest neighbor (KNN) [33]. However, each of these algorithms has its own advantages and disadvantages, and there is an urgent need to find an accurate and rapid method for assessing the paddy layer.…”

Section: Introductionmentioning

confidence: 99%

Identification of Water Layer Presence in Paddy Fields Using UAV-Based Visible and Thermal Infrared Imagery

et al. 2023

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The accurate identification of the water layer condition of paddy fields is a prerequisite for precise water management of paddy fields, which is important for the water-saving irrigation of rice. Until now, the study of unmanned aerial vehicle (UAV) remote sensing data to monitor the moisture condition of field crops has mostly focused on dry crops, and research on the water status of paddy fields has been relatively limited. In this study, visible and thermal infrared images of paddy fields at key growth stages were acquired using a UAV remote sensing platform, and three model input variables were constructed by extracting the color features and temperature features of each field, while K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and logistic regression (LR) analysis methods were applied to establish a model for identifying the water layer presence in paddy fields. The results showed that KNN, SVM, and RF performed well in recognizing the presence of water layers in paddy fields; KNN had the best recognition accuracy (89.29%) via algorithm comparison and parameter preference. In terms of model input variables, using multisource remote sensing data led to better results than using thermal or visible images alone, and thermal data was more effective than visible data for identifying the water layer status of rice fields. This study provides a new paradigm for monitoring the water status of rice fields, which will be key to the precision irrigation of paddy fields in large regions in the future.

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Psoriasis Skin Disease Identification Using Support Vector Machine(SVM) Image Classification and Determining the Growth Rate

Cited by 5 publications

References 7 publications

Machine Learning Assisted Spectral Fingerprinting for Immune Cell Phenotyping

Machine Learning Assisted Spectral Fingerprinting for Immune Cell Phenotyping

Texture segmentation method for computer-assisted dermatologic diagnostic system

Identification of Water Layer Presence in Paddy Fields Using UAV-Based Visible and Thermal Infrared Imagery

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