Integration of morphological preprocessing and fractal based feature extraction with recursive feature elimination for skin lesion types classification

Chatterjee, Saptarshi; Dey, Debangshu; Munshi, Sugata

doi:10.1016/j.cmpb.2019.06.018

Cited by 85 publications

(64 citation statements)

References 50 publications

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“…3 ) using a separate validation set (258 CXR images). In order to select the optimal combination of evaluated classifiers for majority vote, we implemented an exhaustive search using recursive elimination method ( Chatterjee, Dey, & Munshi, 2019 ; Q. Chen, Meng, & Su, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble

Chandra

Verma

Singh

et al. 2021

Expert Systems with Applications

235

190

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Highlights Proposed automatic COVID screening (ACoS) system for detection of infected patients. Random image augmentation is applied to incorporate the variability in the images. Applied hierarchical (two phase) classification to segregate three classes. Majority vote based classifier ensemble is used to combine model’s prediction. Proposed method show promising potential to detect nCOVID-19 infected patients.

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Section: Methodsmentioning

confidence: 99%

Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble

Chandra

Verma

Singh

et al. 2021

Expert Systems with Applications

235

190

View full text Add to dashboard Cite

show abstract

“…These features are grouped into two categories, the reflection of shape (A, B, and D) and color (C) and can detect melanoma lesions giving an accuracy of 72%. A computer-aided method of [10] introduces a fractal-based border irregularity measurement and regional texture analysis technique for extracting the features from Dermoscopic images. A combination of gray level co-occurrence matrix (GLCM) and a proposed fractal-based regional texture analysis (FRTA) algorithm extracts the irregularity of lesion texture.…”

Section: Analysis Of Feature Extraction Techniquesmentioning

confidence: 99%

A Feature Selection Framework for Granular Parakeratosis Lesion Detection

Sheetal¹

2021

IJRASET

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The dermatoscopy images play an essential role in the identification of skin diseases such as granular parakeratosis. This disease appears as an intertrigo or as a non intertriginous rash (intertrigo is nothing but rash in the body folds) and sometimes causes itchy. Extracting the features from skin lesion image is very important for the classification of skin disease. Some features such as shape, size, and color can be extracted from the image depending upon the ABCD (asymmetry, border, color and diameter) rule of dermoscopy. These extracted features are mainly used to determine asymmetry and border irregularity. Asymmetry is one of the main attributes in the early detection of skin disease. The dermatologists inspect shape and irregular color distribution to identify the lesion asymmetry. This paper provides a large set of skin lesion feature extraction approaches for the lesion analysis. Several techniques exist for extracting the features from lesion images and the computational methods have been developed for enhancing the features and allow dermatologists in the earlier diagnosis of the disease. The methods or the procedures adopted by several researchers for extraction of features are explained and the effect of these methods on the skin lesion is evaluated using suitable metrics. Though extraction of the features from the skin lesion has been successfully addressed in several articles, still there is a requirement for the modification or development of new techniques for improving the efficiency of the diagnosis system.

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“…The algorithm calculates a rank score and eliminates the lowest-ranking features. Previous studies showed significant performance improvements by employing RFE, including predicting mental states (brain activity) [31,32], Parkinson [33], skin disease [34], autism [35], Alzheimer [36], and T2D [37]. They showed that SVM-RFE achieved superior performance than several comparison methods.…”

Section: Recursive Feature Eliminationmentioning

confidence: 99%

Deep Neural Network for Predicting Diabetic Retinopathy from Risk Factors

et al. 2020

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Extracting information from individual risk factors provides an effective way to identify diabetes risk and associated complications, such as retinopathy, at an early stage. Deep learning and machine learning algorithms are being utilized to extract information from individual risk factors to improve early-stage diagnosis. This study proposes a deep neural network (DNN) combined with recursive feature elimination (RFE) to provide early prediction of diabetic retinopathy (DR) based on individual risk factors. The proposed model uses RFE to remove irrelevant features and DNN to classify the diseases. A publicly available dataset was utilized to predict DR during initial stages, for the proposed and several current best-practice models. The proposed model achieved 82.033% prediction accuracy, which was a significantly better performance than the current models. Thus, important risk factors for retinopathy can be successfully extracted using RFE. In addition, to evaluate the proposed prediction model robustness and generalization, we compared it with other machine learning models and datasets (nephropathy and hypertension–diabetes). The proposed prediction model will help improve early-stage retinopathy diagnosis based on individual risk factors.

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Integration of morphological preprocessing and fractal based feature extraction with recursive feature elimination for skin lesion types classification

Cited by 85 publications

References 50 publications

Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble

Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble

A Feature Selection Framework for Granular Parakeratosis Lesion Detection

Deep Neural Network for Predicting Diabetic Retinopathy from Risk Factors

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