EEG Signal Analysis Using Different Clustering Techniques

Pradhan, Chinmaya Kumar; Rahaman, Shariar; Sheikh, Abdul Alim; Kole, Alok; Maity, Tanmoy

doi:10.1007/978-981-13-1498-8_9

Cited by 7 publications

(2 citation statements)

References 4 publications

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“…HE applications of Artificial Intelligence (AI) and Machine Learning (ML) have been expanding at an exponential rate [1], [2], [3], [4], [5], [6], [7], [8], [9], [10]. There has been a substantial growth in the applications of AI and ML techniques in various radiological imaging tasks [11], [12].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Enhanced Machine Learning Algorithms for Brain Tumor Detection and Classification

GHOSH¹,

Kole²

2021

Preprint

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<p>The improvement of Artificial Intelligence (AI) and Machine Learning (ML) can help radiologists in tumor diagnostics without invasive measures. Magnetic resonance imaging (MRI) is a very useful method for diagnosis of tumors in human brain. In this paper, brain MRI images have been analyzed to detect the regions containing tumors and classify these regions into three different tumor categories: meningioma, glioma, and pituitary. This paper presents the implementation and comparison of various enhanced ML algorithms for the detection and classification of brain tumors. A brain tumor is the growth of abnormal cells in the human brain. Brain tumors can be cancerous or non-cancerous. Cancerous or malignant brain tumors can be life threatening. Hence, detection and classification of brain tumors at an early stage is extremely important. In this paper, enhanced ML algorithms have been implemented to predict the presence or the absence of brain tumors using binary classification and to predict whether a patient has brain tumor or not and if he does, detect the type of brain tumor using multi-class classification. The dataset that has been used to perform the binary classification task comprises of two types of brain MRI images with tumor and without tumor. Here nine ML algorithms namely, Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbor (KNN), Naïve Bayes (NB), Decision Tree (DT) classifier, Random Forest classifier, XGBoost classifier, Stochastic Gradient Descent (SGD) classifier and Gradient Boosting classifier have been used to classify the MRI images. A comparative analysis of the ML algorithms has been performed based on a few performance metrics such as accuracy, recall, and precision, F1-score, AUC-ROC curve and AUC-PR curve. Gradient Boosting classifier has outperformed all the other algorithms with an accuracy of 92.4%, recall of 94.4%, precision of 85%, F1-score of 89.5%, AUC-ROC of 97.2% and an AUC-PR of 91.4%. To address the multi-class classification problem, four ML algorithms namely, SVM, KNN, Random Forest classifier and XGBoost classifier have been employed. In this case, the dataset that has been used consists of four types of brain MRI images with glioma tumor, meningioma tumor, and pituitary tumor and with no tumor. The performances of the ML algorithms have been compared based on accuracy, recall, precision and the F1-score. XGBoost classifier has surpassed all the other algorithms in terms of accuracy, precision, recall and F1-score. XGBoost has produced an accuracy of 90%, precision of 90%, and recall of 90% and F1-score of 90%.</p>

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Section: Introductionmentioning

confidence: 99%

A Comparative Study of Enhanced Machine Learning Algorithms for Brain Tumor Detection and Classification

GHOSH¹,

Kole²

2021

Preprint

Self Cite

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“…Gregory Piatetsky stated in the Knowledge Discovery (KDD) conference: “Weka is a landmark system in data mining and machine learning history for the research communities, cause it holds the toolkit that has undoubtedly gained such extensive espousal and survived for a prolonged period. [15]” There was substantial attention paid to the determination of how distinctive clustering techniques were utilized in different areas of the environment [16,17,18,19,20] and in the healthcare sector for different disease predictions [21,22,23,24,25,26,27,28,29,30,31,32]. In addition, K-mean and SOM (self-organizing map) were used in this study for grouping the clusters of the real-life diabetes dataset, after careful analysis by the literature.…”

Section: Introductionmentioning

confidence: 99%

A Euclidean Group Assessment on Semi-Supervised Clustering for Healthcare Clinical Implications Based on Real-Life Data

Sohail

Ren

Muhammad

2019

IJERPH

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The grouping of clusters is an important task to perform for the initial stage of clinical implication and diagnosis of a disease. The researchers performed evaluation work on instance distributions and cluster groups for epidemic classification, based on manual data extracted from various repositories, in order to evaluate Euclidean points. This study was carried out on Weka (3.9.2) using 281 real-life health records of diabetes mellitus patients including males and females of ages>20 and <87, who were simultaneously suffering from other chronic disease symptoms, in Nigeria from 2017 to 2018. Updated plugins of K-mean and self-organizing map(SOM) machine learning algorithms were used to cluster the data class of mellitus type for initial clinical implications. The results of the K-mean assessment were built in 0.21 seconds with nine iterations for “type” and eight for “class” attributes. Out of 281 instances, 87 (30.97%) were classified as negative and 194 (69.03%) as positive in the testing on the Euclidean space plot. By assessment for Euclidean points, SOM discovered the search space in a more effective way, but K-mean positioning potencies are impulsive in convergence. This study is important for epidemiological disease diagnosis in countries with a high epidemic risk and low socioeconomic status.

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Improved Visual Analytic Process Under Cognitive Aspects

Bouazizi

Ltifi

2021

Advanced Information Networking and Applications

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EEG Signal Analysis Using Different Clustering Techniques

Cited by 7 publications

References 4 publications

A Comparative Study of Enhanced Machine Learning Algorithms for Brain Tumor Detection and Classification

A Comparative Study of Enhanced Machine Learning Algorithms for Brain Tumor Detection and Classification

A Euclidean Group Assessment on Semi-Supervised Clustering for Healthcare Clinical Implications Based on Real-Life Data

Improved Visual Analytic Process Under Cognitive Aspects

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