Automated Classification of Cancerous Brain Tumours Using Haarlet Transform and Probabilistic Neural Network

Kaur, Manmet; Prajapat, Balwant

doi:10.1007/978-981-13-2673-8_3

Cited by 2 publications

(2 citation statements)

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“…In [21] authors Manmet et.al (2019) have developed an automated technique for malignant brain tumor classification utilizing the HAARLET transform and a probabilistic neural network. In addition to the HAARLET transform, data preprocessing employs threshold-based segmentation and binarization.…”

Section: Literature Of Reviewmentioning

confidence: 99%

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Analysis and development of brain tumor prediction model using deep neural network

Chhabra,

Bansal

2023

ITM Web Conf.

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The human brain consists of billions of living organisms and is very difficult to decipher because of its complexity. Brain tumors can be deadly, significantly impacting the quality of life and changing everything for patients and their loved ones. In today’s world, brain tumors are a leading cause of death in both children and adults. A high death percentage is caused due to the invasive properties of tumors. But it is inspiring that the survival rate might increase if the diagnosis is performed at the early stage [9]. Accurate detection of the brain tumor at an early stage can prolong the chance of survival of an infected patient [4]. Magnetic Resonance Imaging (MRI) is the most popular imaging technique used today for detecting brain tumors. Deep Neural Network techniques plays an important role in detecting brain tumors. This manuscript offers a brief analysis of studies conducted by various authors in the field of BT categorization and diagnosis from MRI images using Deep Neural Network (DNN). This paper also suggests a method for classifying and identifying brain tumors based on MRI pictures and supporting text using DNN and DWT.

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Section: Literature Of Reviewmentioning

confidence: 99%

“…The training data set's feature extraction produces twelve features for each of the training images, which are then utilized to train a probabilistic neural network. The developed approach has been proven to have a classification accuracy of 96.3 percent [21].…”

Section: Literature Of Reviewmentioning

confidence: 99%

Analysis and development of brain tumor prediction model using deep neural network

Chhabra,

Bansal

2023

ITM Web Conf.

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Particle Rider Mutual Information and Dendritic-Squirrel Search Algorithm With Artificial Immune Classifier for Brain Tumor Classification

Chakre

Patil

2021

The Computer Journal

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Magnetic Resonance Images (MRI) is an imperative imaging modality employed in the medical diagnosis tool for detecting brain tumors. However, the major obstacle in MR images classification is the semantic gap between low-level visual information obtained by MRI machines and high-level information alleged by the clinician. Hence, this research article introduces a novel technique, namely Dendritic-Squirrel Search Algorithm-based Artificial immune classifier (Dendritic-SSA-AIC) using MRI for brain tumor classification. Initially the pre-processing is performed followed by segmentation is devised using sparse fuzzy-c-means (Sparse FCM) is employed for segmentation to extract statistical and texture features. Furthermore, the Particle Rider mutual information (PRMI) is employed for feature selection, which is devised by integrating Particle swarm optimization, Rider optimization algorithm and mutual information. AIC is employed to classify the brain tumor, in which the Dendritic-SSA algorithm designed by combining dendritic cell algorithm and Squirrel search algorithm (SSA). The proposed PRMI-Dendritic-SSA-AIC provides superior performance with maximal accuracy of 97.789%, sensitivity of 97.577% and specificity of 98%.

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Automated Classification of Cancerous Brain Tumours Using Haarlet Transform and Probabilistic Neural Network

Cited by 2 publications

References 4 publications

Analysis and development of brain tumor prediction model using deep neural network

Analysis and development of brain tumor prediction model using deep neural network

Particle Rider Mutual Information and Dendritic-Squirrel Search Algorithm With Artificial Immune Classifier for Brain Tumor Classification

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