Brain Diagnoses Detection Using Whale Optimization Algorithm Based on Ensemble Learning Classifier

Fouad, A.; Moftah, Hossam M.; Hefny, Hesham A.

doi:10.22266/ijies2020.0430.05

Cited by 8 publications

(9 citation statements)

References 27 publications

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“…The next stage is the exploitation stage, whereby the whales implement bubble-net hunting. The following equation is utilized for mathematically modeling these behaviors as follows: Whereas represents the best position of the attained solution, characterizes the count of iterations, and the constant variables and are characterized by: (6) (7) Now, denotes an arbitrary vector amongst [0, 1], and variable is an arbitrary vector utilized for controlling the convergence procedure. It can be linearly decreased from two to zero through iteration: (8) In Eq.…”

Section: E Hyperparameter Tuningmentioning

confidence: 99%

“…Generally, a classification study using the ML method needs four stages: feature selection, feature extraction, feature-based classification algorithm selection, and dimensionality reduction [6]. This process requires multiple optimization steps and professional knowledge that might take considerable time [7]. To conquer this problem, deep learning (DL), an emerging field of ML study that uses raw neuroimaging information to generate features through "on-the-fly" learning, is received significant interest in the Area of large-scale, higher-dimension medical imaging analysis.…”

Section: Introductionmentioning

confidence: 99%

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Modeling of Whale Optimization with Deep Learning based Brain Disorder Detection and Classification

Uvaneshwari¹,

Baskar²

2023

IJACSA

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Brain disorders are a significant source of economic strain and unfathomable suffering in modern society. Imaging techniques help diagnose, monitor and treat mental health, neurological, and developmental disorders. To aid in the Computer-Aided Diagnosis of brain diseases, deep learning (DL) was used for the analysis of neuroimages from modalities including Positron Emission Tomography (PET), Structural Magnetic Resonance Imaging (SMRI), and functional MRI. In this study, a Whale Optimization Algorithm is used with Deep Learning to analyse MRI scans for signs of neurological disease. WOADL-BDDC may detect and label abnormalities in the brain based on an MRI scan. It uses a two-step pre-processing procedure, first using guided filtering to get rid of background noise and then using U-Net segmentation to get rid of the top of the head. QuickNAT, along with RMSProp, is used to segment the brain. When analysing data, WOADL-BDDC uses radionics to collect information from every layer. When used in a convolutional recurrent neural network model, the Whale Optimization Algorithm may accurately categorize mental illness. WOADL-BDDC is put through its paces using ADNI 3D. Compared to state-of-the-art classification results from Vgg16, Graph CNN, Modified ResNet18, Non-linear SVM, ResNet50-SVM, ResNet50-RF, the suggested technique achieved the greatest accuracy. It was demonstrated that the suggested model is superior to other models for classification from MRI images. In simulations, the proposed approach is shown to be effective in optimizing hyperparameters with an accuracy of 94.38 % on TR set and 94.87% on TS set, Precision of 96.43% on TR set and 97.62% on TS set, and an F1-Score of 89.35 % and 92.10% on TR and TS set, respectively.

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Section: E Hyperparameter Tuningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling of Whale Optimization with Deep Learning based Brain Disorder Detection and Classification

Uvaneshwari¹,

Baskar²

2023

IJACSA

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“…Fouad [18] developed brain tumour using whale optimization algorithm (WOA) with ensemble learning classifier. The developed approach utilized haar discrete wavelet transforms with histogram of oriented gradients (HDWT-HOG) features formed a hybrid model.…”

Section: Literature Reviewmentioning

confidence: 99%

Brain Tumor Classification for MR Images Using Hybrid GLCM-LDTP-Le-Net Feature extraction and Bi-LSTM model

2022

IJIES

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The MRI is assisted by doctors to evaluate the tumors that help them for planning the required treatment. The physicians are dependent on the medical MRI diagnosis but that required lots of effort in finding out the tumor regions. The existing models speed up the scanning process using the MRI technique that reduced the burdens from radiologists but showed abnormality due to the presence of outliers when extracting hyperparameters. The proposed Hybrid GLCM-LDTP-Le-Net Feature extraction technique is developed by combining the Grey level covariance Matrix (GLCM), Local Direction Ternary Pattern (LDTP) and Le-Net features. The feature extraction technique results with hybrid parameters, which are applied to the Bidirectional Long Short Term Memory (Bi-LSTM) model to classify the brain MRI as a Malignant or Benign. The proposed hybrid feature extraction increases the accuracy of learned models by extracting the combined features from the input data. The proposed hybrid feature extraction obtained an accuracy of 97.88 %, better when compared with the existing CNN-based models' methods that obtained accuracy of 91%.

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“…Proses utama yang digunakan yaitu fitur ekstraksi histogram gradien yang berorientasi pada citra gambar yang di deteksi. Hasil yang didapatkan dalam diagnosis deteksi tumor menggunakan Decision Tree J48 yaitu dapat mengklasifikasikan gambar dengan nilai akurasi mencapai 96,4% [17].…”

Section: Pendahuluanunclassified

Klasifikasi Citra Burung Lovebird Menggunakan Decision Tree dengan Empat Jenis Evaluasi

Rahman

2021

RESTI

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Lovebird is a pet that many people in Indonesia have known. The diversity of species, coat color, and body shape gives it its charm. As well in this lovebird bird has its uniqueness of various rare colors. However, many ordinary people have difficulty distinguishing the types of lovebirds. This research is needed to improve previous study performance in classifying lovebird images using the Decision Tree J48 algorithm with 4 types of evaluation. In this case, also to reduce the stage of feature extraction to speed up the computational process. Based on available comparisons, the results obtained at the same split ratio with a comparison of 60:40 in Decision Tree J48 have the precision of 1,000, recall of 1,000, f-measure of 1,000, and accuracy value of 100%. Then the Artificial Neural Network with a split ratio of 60:40 has a precision of 0.854, recall of 0.843, f-measurement of 0.841, and an accuracy value of 84.25%. These results prove that by testing the first-level extraction on color features, Decision Tree J48 is superior in classifying images of lovebird species, and Decision Tree J48 can improve performance and produce the best accuracy.

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Brain Diagnoses Detection Using Whale Optimization Algorithm Based on Ensemble Learning Classifier

Cited by 8 publications

References 27 publications

Modeling of Whale Optimization with Deep Learning based Brain Disorder Detection and Classification

Modeling of Whale Optimization with Deep Learning based Brain Disorder Detection and Classification

Brain Tumor Classification for MR Images Using Hybrid GLCM-LDTP-Le-Net Feature extraction and Bi-LSTM model

Klasifikasi Citra Burung Lovebird Menggunakan Decision Tree dengan Empat Jenis Evaluasi

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