Brain Tumor Detection From MRI Images Using Bag Of Features And Deep Neural Network

Razzaq, Saqlain; Mubeen, Nimra; Kiran, Urwah; Asghar, Muhammad Adeel; Fawad, Fawad

doi:10.1109/raeecs50817.2020.9265768

Cited by 11 publications

(4 citation statements)

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“…This model achieved 97.8% accuracy rate. In [10], Deep Neural Network (DNN) and SVM approaches were used it helps to enhance the image for detection. The method involves processing the MRI image, preprocessing it in grayscale, scaling the image and then transforming it into a two-dimensional spatial image with a flexible wavelet transform.…”

Section: Literature Reviewmentioning

confidence: 99%

Brain Tumor Detection Using Deep Learning Technique

Nayak

2024

IJRASET

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One of the most aggressive disorders affecting both children and adults is brain tumors. Brain tumors develop very quickly, and if not treated at the proper time, they decrease the patient's chances of survival. It is crucial to find brain tumors at an early stage. To increase patients’ life expectancy, proper treatment planning and precise diagnostics are most important. Magnetic Resonance Imaging (MRI) is the most effective method for finding brain tumors. Therefore, to find the types of tumors, an automated brain tumor detection system is needed [1]. This work uses Deep Learning (DL) architectures like Convolutional Neural Network (CNN) and EfficientNetB0 for Transfer Learning to detect the brain tumor. This model is used to predict the types of brain tumors

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Section: Literature Reviewmentioning

confidence: 99%

Brain Tumor Detection Using Deep Learning Technique

Nayak

2024

IJRASET

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“…The outcome was 99% performance with the proposed model. Another work that contributed to brain-tumor recognition used computer-vision techniques [26] along with machine-learning algorithms. The author proposed the computational framework because manually performing this task may be subject to human error during identification.…”

Section: Literature Reviewmentioning

confidence: 99%

Computer-Aided Early Melanoma Brain-Tumor Detection Using Deep-Learning Approach

et al. 2023

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Brain tumors affect the normal functioning of the brain and if not treated in time these cancerous cells may affect the other tissues, blood vessels, and nerves surrounding these cells. Today, a large population worldwide is affected by the precarious disease of the brain tumor. Healthy tissues of the brain are suspected to be damaged because of tumors that become the most significant reason for a large number of deaths nowadays. Therefore, their early detection is necessary to prevent patients from unfortunate mishaps resulting in loss of lives. The manual detection of brain tumors is a challenging task due to discrepancies in appearance in terms of shape, size, nucleus, etc. As a result, an automatic system is required for the early detection of brain tumors. In this paper, the detection of tumors in brain cells is carried out using a deep convolutional neural network with stochastic gradient descent (SGD) optimization algorithm. The multi-classification of brain tumors is performed using the ResNet-50 model and evaluated on the public Kaggle brain-tumor dataset. The method achieved 99.82% and 99.5% training and testing accuracy, respectively. The experimental result indicates that the proposed model outperformed baseline methods, and provides a compelling reason to be applied to other diseases.

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“…Saqlain et. al [16] an approach based on the Bag of Features (BoF) method for feature selection is introduced in this study. In the beginning, MRI pictures are used to extract hand crafted features.…”

Section: Literature Reviewmentioning

confidence: 99%

Brain Tumor Classification, Segmentation, and Detection using Deep Learning - A Review

Mrs. Deepali K. Jadhav

2023

IJRITCC

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V.Vapnik in 1965 proposed Vector methods. Kimeldorf presented a technique for creating kernel space based on support vectors in 1971. Support Vector Machine (SVM) techniques were initially presented in the 1990s by V. Vapnik in the field of statistical learning. Since then, pattern recognition, natural language processing, image processing and other areas have seen extensive use of SVM. By converting non-linear sample space into linear space via a kernel approach, the algorithm's complexity is reduced. Image classification is a well-known issue in image processing. Predicting the input image categories using the features is the main objective of image classification. There are several different classifiers, including Artificial Neural Networks, Support Vector Machines, and Random Forests, Decision Forests, k-NNs (k Nearest Neighbors), and Adaptive Boost. SVM is one of the best techniques for categorizing any image or pattern. A common non-invasive technique used in the medical sector for the analysis, diagnosis, treatment of brain tissues is magnetic resonance imaging. When a brain tumor is discovered early, the patient's life can be saved by receiving the appropriate care. It becomes difficult to accurately identify tumors in the MRI slices, which requires fussy work..

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Brain Tumor Detection From MRI Images Using Bag Of Features And Deep Neural Network

Cited by 11 publications

References 11 publications

Brain Tumor Detection Using Deep Learning Technique

Brain Tumor Detection Using Deep Learning Technique

Computer-Aided Early Melanoma Brain-Tumor Detection Using Deep-Learning Approach

Brain Tumor Classification, Segmentation, and Detection using Deep Learning - A Review

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