Texture and color feature based WLS framework aided skin cancer classification using MSVM and ELM

Choudhury, Dwaipayan; Naug, Avisek; Ghosh, Susmita

doi:10.1109/indicon.2015.7443780

Cited by 22 publications

(9 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Intuitively speaking, if the image is constituted with similar blocks of pixels' gray value, the GLCM is diagonal elements with relatively large value; if the pixel gray value change in the local area, then the off-diagonal elements will have relatively large values. The energy, correlation and inertia moment can be calculated as [6,22,23]: For rough texture, ij P are close to main diagonal, so that im is smaller, while for fine texture, im is larger. (3) Gray Gradient Co-occurrence Matrix The GGCM includes the extracted texture features by using gray and gradient synthetic information, which is similar in process to the GLCM.…”

Section: Methodology a Texture-based Features Extractionmentioning

confidence: 99%

“…Most researchers have focused on morphological feature based automatic recognition of microscopic images [5], while morphological feature calculations take more of a geometrical analysis on the microscopic images. On the contrary, texture analysis is an important research content for image understanding, analysis, recognition, and description of the difference of the structure, direction, granularity and regularity of the different regions of the microscopic image [6][7]. Moreover, texture contains not only the surface properties/characteristics but also some extent that reflects the relationship between them and the environment.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Texture features based microscopic image classification of liver cellular granuloma using artificial neural networks

Shi

Chen

Wang

et al. 2019

2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

View full text Add to dashboard Cite

Automated classification of Schistosoma mansoni granulomatous microscopic images of mice liver using Artificial Intelligence (AI) technologies is a key issue for accurate diagnosis and treatment. In this paper, three grey difference statistics-based features, namely three Gray-Level Co-occurrence Matrix (GLCM) based features and fifteen Gray Gradient Co-occurrence Matrix (GGCM) features were calculated by correlative analysis. Ten features were selected for three-level cellular granuloma classification using a Scaled Conjugate Gradient Back-Propagation Neural Network (SCG-BPNN) in the same performance. A cross-entropy is then calculated to evaluate the proposed Sigmoid input and the ten-hidden layer network. The results depicted that SCG-BPNN with texture features performs high recognition rate compared to using morphological features, such as shape, size, contour, thickness and other geometry-based features for the classification. The proposed method also has a high accuracy rate of 87.2% compared to the Back-Propagation Neural Network (BPNN), Back-Propagation Hopfield Neural Network (BPHNN) and Convolutional Neural Network (CNN).

show abstract

Section: Methodology a Texture-based Features Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Texture features based microscopic image classification of liver cellular granuloma using artificial neural networks

Shi

Chen

Wang

et al. 2019

2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

View full text Add to dashboard Cite

show abstract

“…Illumination correction [43,44,[46][47][48][49]51,[53][54][55][56]59,63,65,66] Artifact removal [23,27,39,[41][42][43][53][54][55][56]59,63,67,71] Data augmentation [28,31,35,36,40,51,55,62,73] Image cropping [23,25,28,42,[46][47][48][49]69] 4.2.1. Illumination Correction (Shading Attenuation)…”

Section: Pre-processing Task Referencesmentioning

confidence: 99%

Automatic Skin Cancer Detection Using Clinical Images: A Comprehensive Review

Nazari,

Garcia

2023

Life

View full text Add to dashboard Cite

Skin cancer has become increasingly common over the past decade, with melanoma being the most aggressive type. Hence, early detection of skin cancer and melanoma is essential in dermatology. Computational methods can be a valuable tool for assisting dermatologists in identifying skin cancer. Most research in machine learning for skin cancer detection has focused on dermoscopy images due to the existence of larger image datasets. However, general practitioners typically do not have access to a dermoscope and must rely on naked-eye examinations or standard clinical images. By using standard, off-the-shelf cameras to detect high-risk moles, machine learning has also proven to be an effective tool. The objective of this paper is to provide a comprehensive review of image-processing techniques for skin cancer detection using clinical images. In this study, we evaluate 51 state-of-the-art articles that have used machine learning methods to detect skin cancer over the past decade, focusing on clinical datasets. Even though several studies have been conducted in this field, there are still few publicly available clinical datasets with sufficient data that can be used as a benchmark, especially when compared to the existing dermoscopy databases. In addition, we observed that the available artifact removal approaches are not quite adequate in some cases and may also have a negative impact on the models. Moreover, the majority of the reviewed articles are working with single-lesion images and do not consider typical mole patterns and temporal changes in the lesions of each patient.

show abstract

“…Texture analysis is an important research content of image understanding, analysis and recognition. Texture can be used to describe the difference of the structure, direction, granularity and regularity of the different regions of the image [34]. The texture contains not only the surface properties or characteristics, but also the extent.…”

Section: Introductionmentioning

confidence: 99%

Plantar pressure image fusion for comfort fusion in diabetes mellitus using an improved fuzzy hidden Markov model

Wang

Dey

et al. 2019

Biocybernetics and Biomedical Engineering

View full text Add to dashboard Cite

Diabetes mellitus is a clinical syndrome caused by the interaction of genetic and environmental factors. The change of plantar pressure in diabetic patients is one of the important reasons for the occurrence of diabetic foot. The abnormal increase of plantar pressure is a predictor of the common occurrence of foot ulcers. The feature extraction of plantar pressure distribution will be beneficial to the design and manufacture of diabetic shoes that will be beneficial for early protection of Diabetes mellitus patients. In this research, texture-based features of the Angular Second Moment (ASM), Moment of Inertia (MI), Inverse Difference Monument (IDM), and Entropy (E) have been selected and fused by using the an up-down algorithm. The fused features are normalized to predict comfort plantar pressure imaging dataset using an improved Fuzzy Hidden Markov Model (FHMM). In FHMM, type-I fuzzy set is proposed and Fuzzy Baum-Welch algorithm is also applied to estimate the next features. The results are discussed, and by comparing with other back-forward algorithms and different fusion operations in FHMM. Improved HMMs with up-down fusion using type-I fuzzy definition performs high effectiveness in prediction comfort plantar pressure distribution in an image dataset with an accuracy of 82.2% and the research will be applied to the shoe-last personalized customization in the industry.

show abstract

Texture and color feature based WLS framework aided skin cancer classification using MSVM and ELM

Cited by 22 publications

References 16 publications

Texture features based microscopic image classification of liver cellular granuloma using artificial neural networks

Texture features based microscopic image classification of liver cellular granuloma using artificial neural networks

Automatic Skin Cancer Detection Using Clinical Images: A Comprehensive Review

Plantar pressure image fusion for comfort fusion in diabetes mellitus using an improved fuzzy hidden Markov model

Contact Info

Product

Resources

About