BSS-Based Feature Extraction for Skin Lesion Image Classification

Stockmeier, H.; Bäcker, H.; Bäumler, Wolfgang; Lang, Elmar

doi:10.1007/978-3-642-00599-2_59

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…To further reduce the unnecessary details researchers used wavelet packet transform [50], grey level co-occurrence matrix [51,52], principal component analysis [53], decision boundary [44,54], Fourier power spectrum [55] and Gaussian derivative kernels [56]. In general, dyadic Gabor [57], Law Mask and Wavelet Transform [58] are used for filters. It is observed that most of the time the features extraction process is subject to error [9].…”

Section: Segmentation and Feature Extractionmentioning

confidence: 99%

Towards the Performance Investigation of Automatic Melanoma Diagnosis Applications

Asif¹,

Fatima²,

Anjum³

et al. 2019

ijacsa

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Melanoma is a type of skin cancer, one of the fatal diseases that appear as an abnormal growth of skin cells and the lesion part often looks like a mole on the skin. Early detection of melanoma from skin lesion by means of screening is an important step towards a reduction in mortality. For this purpose, numerous automatic melanoma diagnosis models based on image processing and machine learning techniques are available for computer-based applications (CBA) and smartphone-based applications (SBA). Since, the smartphones are available as most accessible and easiest methods with built-in camera option, SBA are preferred over CBA. In this paper, we explored the available literature and highlighted the challenges of SBA in terms of execution time due to the limited computing power of smartphones. To resolve this issue of storage of the smartphones, we proposed to develop an SBA that can seamlessly process the image data on the cloud instead of local hardware of the smartphone. Therefore, we designed a study to build a machine learning model of melanoma diagnosis to measure the time taken in preprocessing, segmentation, feature extraction, and classification on the cloud and compared the results with the processing time on the smartphone's local machine. The results showed there is a significant difference of p value <0.001 on the average processing time taken on both environments. As the processing on the cloud is more efficient. The findings of the proposed research will be helpful for the developers to decide the processing platform while developing smartphone applications for automatic melanoma diagnosis.

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Section: Segmentation and Feature Extractionmentioning

confidence: 99%

Towards the Performance Investigation of Automatic Melanoma Diagnosis Applications

Asif¹,

Fatima²,

Anjum³

et al. 2019

ijacsa

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show abstract

“…A person may suffer from the disease for months, immediately resolve and recur, and sometimes develop into fissures and erosion due to irritation. Figure 1 shows the sample of Granular parakeratosis (erythematous papules involving the abdomen) Figure 1: The Granular parakeratosis sample In recent techniques of medical imaging, there are several computational solutions available that aid clinicians to interpret the acquired images [2]. Considering the dermatology field, an assorted number of software solutions are available that is proved to be useful in the identification of skin disease or classification of a lesion from non-lesion.…”

Section: Introductionmentioning

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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Features extracting from fluorescence images and features sequential scanning for classification purposes

Odeh

2012

2012 International Conference on Multimedia Computing and Systems

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BSS-Based Feature Extraction for Skin Lesion Image Classification

Cited by 3 publications

References 11 publications

Towards the Performance Investigation of Automatic Melanoma Diagnosis Applications

Towards the Performance Investigation of Automatic Melanoma Diagnosis Applications

A Feature Selection Framework for Granular Parakeratosis Lesion Detection

Features extracting from fluorescence images and features sequential scanning for classification purposes

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