Retraction Note: A method of progression detection for glaucoma using K-means and the GLCM algorithm toward smart medical prediction

Vimal, S.; Robinson, Yohan; Kaliappan, M.; Vijayalakshmi, K.; Seo, Sanghyun

doi:10.1007/s11227-022-04854-0

Cited by 3 publications

(1 citation statement)

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“…This improves the quality of the brain images which also optimize the brain tumor classification rate. Further, the Grey Level Cooccurrence Matrix (GLCM) features (Vimal et al, [20]) are computed with respect to 45 degree angle of orientation. From this constructed GLCM, energy, contrast, correlation and inertia features are computed and they are stored in a matrix which is fed to the next classification module in this paper.…”

Section: Preprocessing and Feature Computationsmentioning

confidence: 99%

Detection and Segmentation of Meningioma Tumors Using the Proposed MENCNN Model

John Nisha Anita,

Sujatha Kumaran

2023

ARASET

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This paper develops a Meningioma Detection and Segmentation System (MDSS) using the proposed Meningioma Convolutional Neural Network (MENCNN) classifier. The main objective of this paper is to detect and locate the meningioma brain tumors using the proposed deep learning structure and segmentation algorithm. This proposed MDSS is designed with preprocessing of meningioma and healthy brain MRI images, feature computations and feature classification through the proposed MENCNN classifier and Meningioma Segmentation Algorithm. The noises in both meningioma and healthy brain images are removed using Mean Adaptive Filter (MAF) and the meningioma features are computed from the noise removed image. These meningioma features are classified by the proposed MENCNN classifier in order to obtain the classification results as either meningioma or healthy brain image. Finally, Meningioma Segmentation Algorithm (MSA) is proposed in this research work to segment the pixels belonging to the meningioma region. The proposed MDSS approach obtains 96.46% MSI, 97.75% MSR and 97.6% MSA on the set of meningioma images in Nanfang dataset. The proposed MDSS approach obtains 97.76% MSI, 98.03% MSR and 97.81% MSA on the set of meningioma images in Kaggle dataset.

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Section: Preprocessing and Feature Computationsmentioning

confidence: 99%

Detection and Segmentation of Meningioma Tumors Using the Proposed MENCNN Model

John Nisha Anita,

Sujatha Kumaran

2023

ARASET

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An Intelligent handcrafted feature selection using Archimedes optimization algorithm for facial analysis

Neggaz

Fizazi

2022

Soft Comput

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Human facial analysis (HFA) has recently become an attractive topic for computer vision research due to technological progress and mobile applications. HFA explores several issues as gender recognition (GR), facial expression, age, and race recognition for automatically understanding social life. This study explores HFA from the angle of recognizing a person’s gender from their face. Several hard challenges are provoked, such as illumination, occlusion, facial emotions, quality, and angle of capture by cameras, making gender recognition more difficult for machines. The Archimedes optimization algorithm (AOA) was recently designed as a metaheuristic-based population optimization method, inspired by the Archimedes theory’s physical notion. Compared to other swarm algorithms in the realm of optimization, this method promotes a good balance between exploration and exploitation. The convergence area is increased By incorporating extra data into the solution, such as volume and density. Because of the preceding benefits of AOA and the fact that it has not been used to choose the best area of the face, we propose utilizing a wrapper feature selection technique, which is a real motivation in the field of computer vision and machine learning. The paper’s primary purpose is to automatically determine the optimal face area using AOA to recognize the gender of a human person categorized by two classes (Men and women). In this paper, the facial image is divided into several subregions (blocks), where each area provides a vector of characteristics using one method from handcrafted techniques as the local binary pattern (LBP), histogram-oriented gradient (HOG), or gray-level co-occurrence matrix (GLCM). Two experiments assess the proposed method (AOA): The first employs two benchmarking datasets: the Georgia Tech Face dataset (GT) and the Brazilian FEI dataset. The second experiment represents a more challenging large dataset that uses Gallagher’s uncontrolled dataset. The experimental results show the good performance of AOA compared to other recent and competitive optimizers for all datasets. In terms of accuracy, the AOA-based LBP outperforms the state-of-the-art deep convolutional neural network (CNN) with 96.08% for the Gallagher’s dataset.

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