An Approach for Enhancement of MR Images of Brain Tumor

Mishra, Hare Krishna; Kaur, Manpreet

doi:10.18280/ts.390405

Cited by 2 publications

(2 citation statements)

References 51 publications

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“…The lack of sufficient number of specialists worldwide and the diagnosis of the disease in advanced stages negatively affect the patient's life. Manual examination of brain tumor images requires a lot of expertise [27,28]. Sometimes, specialists can make erroneous diagnoses while examining MR images.…”

Section: Discussionmentioning

confidence: 99%

Automated Classification of Brain Tumor Disease with a Novel CNN Relief and SVM-Based Deep Hybrid Model

Bayram¹,

Bingol²,

Alataş³

2023

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The brain tumor is a very dangerous type of cancer that can be seen in people of almost any age and usually results in the patient's death. Early detection of these tumors, which have many varieties, is extremely important in terms of the patient's survival, affecting the planning of treatment, just as with other types of cancer. Early diagnosis of the disease is usually performed by means of imaging devices. It takes a lot of expertise to analyze the MRI images and diagnose the brain tumor. In this study, a hybrid deep model is recommended that can be used effectively in the classification of the brain tumor. The proposed hybrid model is a Convolutional Neural Network (CNN)-based method that automatically classifies Magnetic Resonance (MR) images of three different types of brain tumors, Glioma, Meningioma and Pituitary successfully. Our model is basically going through these stages. First of all, the features from the two models that show the highest performance from pre-trained deep models are combined. The most effective features of the specification map obtained in the next phase were selected using the Relief method. At the last stage, classification was carried out with Support Vector Machine (SVM), one of the most known machine learning techniques. As a result of the experiments, the hybrid deep model we proposed obtained 93.2% accuracy. It seems that proposed hybrid method has very competitive results and is thought to be efficiently used to classify the brain tumor.

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Section: Discussionmentioning

confidence: 99%

Automated Classification of Brain Tumor Disease with a Novel CNN Relief and SVM-Based Deep Hybrid Model

Bayram¹,

Bingol²,

Alataş³

2023

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“…In the study [32], the authors propose a comprehensive learning-based elephant herding optimization technique. The paper focuses on determining the optimal values of the smoothness factor in bi-histogram equalization using adaptive sigmoid functions.…”

Section: Related Workmentioning

confidence: 99%

Enhancement of Pre-Trained Deep Learning Models to Improve Brain Tumor

Ullah,

Odeh,

Khattak

et al. 2023

IJCAI

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The early detection of brain tumors is crucial due to their highly dangerous nature and the potential for life-threatening consequences if left undiagnosed. Brain tumors significantly shorten life expectancy and cause extensive damage. To accurately diagnose brain tumors, medical imaging techniques such as MRI and other diagnostic tests play a vital role in the classification process. Artificial intelligence, specifically deep learning and computer vision, offers valuable techniques for detecting and classifying brain tumors. In this research, our focus is on developing an improved methodology for brain tumor classification. We implemented a proposed model using five pre-trained models: CNN, ResNet101, InceptionV3, VGG16, and VGG19. By employing data augmentation techniques, we enhanced their performance. The achieved Precision, Recall, and F1-Measure for unseen images were 95%, 95%, 95%, 97%, and 95%, respectively, as tested using three open datasets. Furthermore, aside from improving early tumor detection, these accuracy improvements have the potential to reduce disabilities such as paralysis. Data augmentation, accomplished through image rotation, scaling, and flipping, proves to be an effective means of enhancing model performance by generating new images with improved quality. Povzetek: Za izboljšanje zgodnjega odkrivanja možganskih tumorjev so uporabili pet globokih nevronskih mrež in dosegli signifikantno izboljšanje.

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An Approach for Enhancement of MR Images of Brain Tumor

Cited by 2 publications

References 51 publications

Automated Classification of Brain Tumor Disease with a Novel CNN Relief and SVM-Based Deep Hybrid Model

Automated Classification of Brain Tumor Disease with a Novel CNN Relief and SVM-Based Deep Hybrid Model

Enhancement of Pre-Trained Deep Learning Models to Improve Brain Tumor

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