Color RGB and Structure GLCM Method to Feature Extraction System in Endoscope Image for The Diagnosis Support of Otitis Media Disease

Resita, Ersa; Sigit, Riyanto; Harsono, Tri; Rahmawati, Rosydiah

doi:10.1109/ies50839.2020.9231532

Cited by 4 publications

(2 citation statements)

References 1 publication

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“…• It also determines how much variation there is in the image at a local level. • The contrast value is a visual measure of variation between grey degrees in an image area [9].…”

Section: Contrastmentioning

confidence: 99%

Skin Type Classifier

Parshionikar¹

2022

IJSREM

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Variations in skin diseases largely fall into the subset of skin types. Classification of Skin Types plays an essential part in the classification of skin diseases. The paper proposes an end to-end machine learning model used to classify skin type as dry, oily, or normal using Python Libraries. Skin type classification is done using Gray-Level Co-Occurrence Matrix (GLCM) for texture identification and Light Gradient Boosting Machine (LGBM). Red Green Blue (RGB) images are first resized and then converted into Gray-Scale images and then features like energy, correlation, dissimilarity, homogeneity, contrast, and entropy are extracted from a given image using GLCM. Such features give us a basic understanding of the type and texture of the image. The GLCM of the image is calculated by changing the distance and angle of the pixels within the image in a regular interval. These features after extraction are fed to the LGBM algorithm. The trained model is then deployed using Flask. Key Words: features extraction, GLCM, LGBM, skin type

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“…• It also determines how much variation there is in the image at a local level. • The contrast value is a visual measure of variation between grey degrees in an image area [9].…”

Section: Contrastmentioning

confidence: 99%

Skin Type Classifier

Parshionikar¹

2022

IJSREM

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“…The visualization model with the concept of machine learning (ML) was developed to perform of the classification of Otitis media and presents quite good results [7]. In a different concept, using an extraction system method based on red, green, and blue (RGB) colors is also able to present fairly accurate classification results [8]. Analysis using classification algorithms can also provide significant results in presenting the classification process based on images of Otitis disease [9].…”

Section: Introductionmentioning

confidence: 99%

Optimization deep learning with rough set approach model classification Otitis

Arief Wisky,

Ade Putra

2023

IJEECS

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<span>Otitis is a disease that occurs in the middle ear in the form of inflammation. This research aims to develop an analysis model for the classification of Otitis disease based on knowledge patterns based on symptoms and type of disease. The analysis methods used include the performance of the certainty factor (CF), rough set (RS), artificial neural network (ANN), and decision tree (DT) methods. CF and RS performance can be used to generate classification rule patterns. These rule patterns become new knowledge in the classification analysis process using the concept of deep learning (DL). DL analysis with ANN and DT performance can work optimally in exploring and discovering hidden knowledge. Based on the results of performance testing, the combination of CF and RS in preprocessing can present a classification pattern of 106 rules. The output of DL analysis results is proven to produce precise and accurate classification results with an accuracy of 89%. Based on these results, the analytical model developed was proven to be effective in classifying Otitis disease. Not only that, this research is also able to contribute to updating the knowledge-based system in the classification process.</span>

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Object Detection and Foreground Extraction in Thermal Images

Srihari¹,

Jonnadula²

2022

Lecture Notes in Electrical Engineering

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Color RGB and Structure GLCM Method to Feature Extraction System in Endoscope Image for The Diagnosis Support of Otitis Media Disease

Cited by 4 publications

References 1 publication

Skin Type Classifier

Skin Type Classifier

Optimization deep learning with rough set approach model classification Otitis

Object Detection and Foreground Extraction in Thermal Images

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