Detection of brain tumor using modified particle swarm optimization (MPSO) segmentation via haralick features extraction and subsequent classification by KNN algorithm

Deepa, G.; Mary, G. Leena Rosalind; Karthikeyan, A.; Rajalakshmi, P.; Hemavathi, K.; Dharanisri, M.

doi:10.1016/j.matpr.2021.10.475

Cited by 9 publications

(6 citation statements)

References 10 publications

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“…Particle swarm optimization (PSO) is an intelligent algorithm designed by simulating the hunting behavior of a group of birds [59] , [60] , [61] . It uses the sharing of information by individuals in a population to make the entire movement of the group in the problem solution space from disorder to order evolutionary process to obtain the optimal solution.…”

Section: Methodsmentioning

confidence: 99%

Strength prediction and application of cemented paste backfill based on machine learning and strength correction

Zhang

et al. 2022

Heliyon

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Section: Methodsmentioning

confidence: 99%

Strength prediction and application of cemented paste backfill based on machine learning and strength correction

Zhang

et al. 2022

Heliyon

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“…The effect of the of hidden layer nodes on the LSTM is discussed in Section 4.1.3 and is not repeated here. To improve the performance of LSTM, the hidden layer nodes and the learning rate are optimized using GWO, PSO, and the sparrow search algorithm (SSA), which have been widely used in industry and academia with excellent results [63][64][65]. Due to the limitations of space, the principles and formulas of the optimization algorithm are not repeated in this section, and interested readers can refer to the references [66][67][68][69].…”

Section: Optimization Of Lstmmentioning

confidence: 99%

Strength Investigation and Prediction of Superfine Tailings Cemented Paste Backfill Based on Experiments and Intelligent Methods

Zhang

et al. 2023

Materials

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The utilization of solid waste for filling mining presents substantial economic and environmental advantages, making it the primary focus of current filling mining technology development. To enhance the mechanical properties of superfine tailings cemented paste backfill (SCPB), this study conducted response surface methodology experiments to investigate the impact of various factors on the strength of SCPB, including the composite cementitious material, consisting of cement and slag powder, and the tailings’ grain size. Additionally, various microanalysis techniques were used to investigate the microstructure of SCPB and the development mechanisms of its hydration products. Furthermore, machine learning was utilized to predict the strength of SCPB under multi-factor effects. The findings reveal that the combined effect of slag powder dosage and slurry mass fraction has the most significant influence on strength, while the coupling effect of slurry mass fraction and underflow productivity has the lowest impact on strength. Moreover, SCPB with 20% slag powder has the highest amount of hydration products and the most complete structure. When compared to other commonly used prediction models, the long-short term memory neural network (LSTM) constructed in this study had the highest prediction accuracy for SCPB strength under multi-factor conditions, with root mean square error (RMSE), correlation coefficient (R), and variance account for (VAF) reaching 0.1396, 0.9131, and 81.8747, respectively. By optimizing the LSTM using the sparrow search algorithm (SSA), the RMSE, R, and VAF improved by 88.6%, 9.4%, and 21.9%, respectively. The research results can provide guidance for the efficient filling of superfine tailings.

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“…The DWT-Coefficient represents the difference between the wavelet function and the analyzed signal of the image. A few other texture-based features like entropy, local binary pattern (LBP) and Haralick features were also used in this paper [63,65]. The entropy computes the randomness of the pixels, LBP represents the texture of the image by thresholding neighbor pixels based on a specific pixel and the Haralick features provides the texture of the image from the normalization of the GLCM.…”

Section: Image Featuresmentioning

confidence: 99%

Brain tumor detection and segmentation: Interactive framework with a visual interface and feedback facility for dynamically improved accuracy and trust

et al. 2023

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Brain cancers caused by malignant brain tumors are one of the most fatal cancer types with a low survival rate mostly due to the difficulties in early detection. Medical professionals therefore use various invasive and non-invasive methods for detecting and treating brain tumors at the earlier stages thus enabling early treatment. The main non-invasive methods for brain tumor diagnosis and assessment are brain imaging like computed tomography (CT), positron emission tomography (PET) and magnetic resonance imaging (MRI) scans. In this paper, the focus is on detection and segmentation of brain tumors from 2D and 3D brain MRIs. For this purpose, a complete automated system with a web application user interface is described which detects and segments brain tumors with more than 90% accuracy and Dice scores. The user can upload brain MRIs or can access brain images from hospital databases to check presence or absence of brain tumor, to check the existence of brain tumor from brain MRI features and to extract the tumor region precisely from the brain MRI using deep neural networks like CNN, U-Net and U-Net++. The web application also provides an option for entering feedbacks on the results of the detection and segmentation to allow healthcare professionals to add more precise information on the results that can be used to train the model for better future predictions and segmentations.

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Detection of brain tumor using modified particle swarm optimization (MPSO) segmentation via haralick features extraction and subsequent classification by KNN algorithm

Cited by 9 publications

References 10 publications

Strength prediction and application of cemented paste backfill based on machine learning and strength correction

Strength prediction and application of cemented paste backfill based on machine learning and strength correction

Strength Investigation and Prediction of Superfine Tailings Cemented Paste Backfill Based on Experiments and Intelligent Methods

Brain tumor detection and segmentation: Interactive framework with a visual interface and feedback facility for dynamically improved accuracy and trust

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