An Analysis of Lung Tumor Classification Using SVM and ANN with GLCM Features

Patel, Vaibhavi; Shah, Samkit; Trivedi, Harshal; Naik, Urja

doi:10.1007/978-981-15-3369-3_21

Cited by 6 publications

(2 citation statements)

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“…Gray level co-occurrence matrix (GLCM) texture analysis is a captive of image texture proposed [10]. GLCM is a method that can operate co-occurrence greyscale matrices through a statistical method to extract any attribute from the image [11]. However, this algorithm is lacks of relationship between texture patterns and sensitivity to image noise [12].…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Detection for Chest X-Ray Images using Local Binary Pattern

Sabri¹

2020

IJETER

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The increase in patients with COVID-19 is overwhelming in healthcare systems around the world. Due to the large number of people affected by this pandemic, the medical and healthcare departments are facing a delay in the detection of COVID-19. Besides, it is not an easy task to clarify the images from the radiograph on what types of infection between bacteria pneumonia and COVID-19. The automatic feature analysis can help physicians more precisely in the treatment and diagnosis of diseases. In this research, Local Binary Pattern (LBP) texture features algorithm has been proposed to automate the current manual approach. This process starts by extracting the intensity grayscale texture from the normal, bacteria pneumonia and COVID-19 chest x-ray images. To prove the accuracy of LBP, a simple classifier k-Nearest Neighbour (k-NN) has been implement to classify the chest x-ray images into normal, bacterial and pneumonia class. The 10-fold cross validation has been used to validate the chest x-ray images. From the evaluation, 96% accuracy can be achieved by using LBP as a feature extraction feature. It shows that LBP is a powerful texture features to detect COVID-19 from the x-ray images. More samples will be collected in the future and neural network approach is suggested as a classifier in the future due to its ability to imitate human respond.

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Section: Introductionmentioning

confidence: 99%

COVID-19 Detection for Chest X-Ray Images using Local Binary Pattern

Sabri¹

2020

IJETER

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“…ith colour channel at the jth pixel as 𝑃 𝑖𝑗 . The four colour moments are calculated as[40]: Moment 1-Mean Mean is the average colour value in the image. It is the square root of variance which denotes the distribution of colour in an image and is calculated by 𝜎 𝑖 Skewness is defined as the degree of asymmetry in the colour distribution.…”

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confidence: 99%

Dermatologist-Level Classification of Skin Cancer Using Cascaded Ensembling of Convolutional Neural Network and Handcrafted Features Based Deep Neural Network

et al. 2022

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Skin cancer is caused due to unusual development of skin cells and deadly type cancer. Early diagnosis is very significant and can avoid some categories of skin cancers, such as melanoma and focal cell carcinoma. The recognition and the classification of skin malignant growth in the beginning time is expensive and challenging. The deep learning architectures such as recurrent networks and convolutional neural networks (ConvNets) are developed in the past, which are proven appropriate for non-handcrafted extraction of complex features. To additional expand the efficiency of the ConvNet models, a cascaded ensembled network that uses an integration of ConvNet and handcrafted features based multi-layer perceptron is proposed in this work. This offered model utilizes the convolutional neural network model to mine nonhandcrafted image features and colour moments and texture features as handcrafted features. It is demonstrated that accuracy of ensembled deep learning model is improved to 98.3% from 85.3% of convolutional neural network model.

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