Quick Reduct-ACO based feature selection for TRUS prostate cancer image classification

Manavalan, R.; Thangavel, K.

doi:10.1109/icprime.2012.6208367

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…The run length of a GLRLM is determined by the level of contiguous pixels in a given direction. Counting the instances of the succeeding run within the image will allow you to approximate it.The count of travels only with the gray value i and lengths j which occur in the image along the angle θ is represented by the (i, j)th element of the gray value run length matrixP (i, j) [15].…”

Section: Gray Level Run-length Matrix (Glrlm)mentioning

confidence: 99%

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On Performance Analysis Of Diabetic Retinopathy Classification

Jafari

et al. 2024

ELCVIA

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This paper describes the Classification of bulk OCT retinal fundus images of normal and diabetic retinopathy using the Intensity histogram features, Gray Level Co-Occurrence Matrix (GLCM), and the Gray Level Run Length Matrix (GLRLM) feature extraction techniques. Three features—Intensity histogram features, GLCM, and GLRLM were taken and, that features were compared fairly. A total of 301 bulk OCT retinal fundus color images were taken for two different varieties which are normal and diabetic retinopathy. For classification and feature extraction, a filtered image output based on a fourth-order PDE is used. Using OCT retinal fundus images, the most effective feature extraction method is identified.

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Section: Gray Level Run-length Matrix (Glrlm)mentioning

confidence: 99%

“…The Gaussian Mixture Model is one of the machine learning techniques (GMM). They are used to group data according to the probability distribution into two categories [15]. Applications for Gaussian mixture models are numerous.…”

Section: Classificationmentioning

confidence: 99%

On Performance Analysis Of Diabetic Retinopathy Classification

Jafari

et al. 2024

ELCVIA

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An exploration of the key determinants for the application of AI-enabled higher education based on a hybrid Soft-computing technique and a DEMATEL approach

2023

Expert Systems with Applications

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Lung Cancer Nodules Detection Using Ideal Features Extraction Technique in CT Images

Pawar,

Premchand,

Govardhanrao

2023

Advances in Intelligent Systems Research

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In the present time, worldwide the number of patients related to lung cancer disease getting increase exponentially, accordingly the application of Computer Aided Diagnosis (CAD) system building association with medical science to deliver pertinent solution using Image Processing and Machine Learning Techniques. This paper presenting a model for Lung Cancer nodules detection in CT image by employing proposed work in the progressive phases, the first step is image pre-processing which uses standard LIDC-IDRI images as input images, the preprocessing approach employ the denoising technique to remove the speckle noises from images, then by applying adaptive contrast enhancement (CLAhe) technique the quality of input CT image is improved. The second step works on to segment the Region of Interest (ROI) using LevelSet segmentation algorithm, the third step employed the learnable Feature Extraction technique from suspected (ROI) in CT image, the feature to be extracted from CT images are Texture Features such as Grey-Level Co-occurrence Matrix (GLCM), Grey-Level Run-Length Matrix (GLRM), Local Binary Pattern (LBP), Shape-Based Features such as Perimeter, Area, Irregularity Index, Solidity, Equivalent Diameter, Convex Area, and Statistical Features such as Mean, Median, Mode, Entropy, Moment, Skewness, and Kurtosis. The optimized measurable features are offered as input to the Hybrid-Layer Convolutional Neural Network, Hybrid-CNN applied Enhanced Cat-Swarm Optimization algorithm for optimal weight selection. The convolutional neural network trained based on feature values by varying the training percentage of dataset (in the range of 50%, 60%, 70%, 80%, and 90%) and the proposed model attains the elevated accuracy of 92.93%. The performance evaluation metrics are used such as Sensitivity, Specificity, Accuracy and F-Measure to evaluate the robustness of proposed Hybrid-CNN classification model.

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Quick Reduct-ACO based feature selection for TRUS prostate cancer image classification

Cited by 3 publications

References 22 publications

On Performance Analysis Of Diabetic Retinopathy Classification

On Performance Analysis Of Diabetic Retinopathy Classification

An exploration of the key determinants for the application of AI-enabled higher education based on a hybrid Soft-computing technique and a DEMATEL approach

Lung Cancer Nodules Detection Using Ideal Features Extraction Technique in CT Images

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