Brain Tumor Diagnosis Support System: A Decision Fusion Framework

Shantta, Kalifa; Basir, Otman

doi:10.21013/jas.v15.n3.p1

Cited by 2 publications

(3 citation statements)

References 13 publications

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“…The results obtained in the previous work "Brain Tumor Diagnosis Support System: A Decision Fusion Framework" [2] are listed in Table 1.…”

Section: Experiments Results and Discussionmentioning

confidence: 99%

“…The 3.6 Python programming language under Anaconda platform software was used to implement the CNN algorithm. We will compare and evaluate the CNN algorithm results with the results obtained in previous work "Brain Tumor Diagnosis Support System: A Decision Fusion Framework" [2]. In previous work, the applied classifiers were K-nearest neighbor KNN, support vector machine SVM, and artificial neuron network ANN to detect the brain tumor employing MRI images.…”

Section: Methodsmentioning

confidence: 99%

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Deep Learning Feature Extraction for Brain Tumor Characterization and Detection

Basir

Shantta

2021

IRA-JAS

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Deep Learning is a growing field of artificial intelligence that has become an operative research topic in a wide range of disciplines. Today we are witnessing the tangible successes of Deep Learning in our daily lives in various applications, including education, manufacturing, transportation, healthcare, military, and automotive, etc.<strong> </strong>Deep Learning is a subfield of Machine Learning that stems from Artificial Neural Networks, where a cascade of layers is employed to progressively extract higher-level features from the raw input and make predictive guesses about new data. This paper will discuss the effect of attribute extraction profoundly inherent in training<strong> </strong>approaches such as Convolutional Neural Networks (CNN). Furthermore, the paper aims to offer a study on Deep Learning techniques and attribute extraction methods that have appeared in the last few years. As the demand increases, considerable research in the attribute extraction assignment has become even more instrumental. Brain tumor characterization and detection will be used as a case study to demonstrate Deep Learning CNN's ability to achieve effective representational learning and tumor characterization.

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“…The results obtained in the previous work "Brain Tumor Diagnosis Support System: A Decision Fusion Framework" [2] are listed in Table 1.…”

Section: Experiments Results and Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Deep Learning Feature Extraction for Brain Tumor Characterization and Detection

Basir

Shantta

2021

IRA-JAS

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“…Shantta and Basir [4] proposed a CNN recognition process which can acknowledge people based on face-to-face illustrations. The model first uses CNN to extract the attributes of the facet and then fuses the abstract characteristics [5] with the hue characteristics [6].…”

Section: Literature Surveymentioning

confidence: 99%

Accurate Brain Tumor Recognition Using Double-Weighted Feature Extraction Labelling Model with Priority Weighted Feature Selection

Revathi¹,

Muddana²

2021

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In clinical practice and patient survival rates, early diagnosis of brain tumors plays a key role. Different forms of brain tumors and their properties and treatments are available. Therefore, tumor detection is complicated, time consuming and error-prone with manual brain tumor detection. Therefore, high-precision automated, computerized diagnostics are currently necessary. Feature extraction is a tumor prediction method for capturing the visual content of a picture. The extraction of features is the process through which the raw image is reduced and decisions like the pattern classification are facilitated. The MRI brain images are considered to be classified as a robust and more accurate classification that is able to serve as an expert assistant for healthcare practitioners. In this research, a new method for selecting and extracting features is introduced. The paper proposes to take into account the most important features for the classification of tumor and non-tumor cells using a Double-Weighted Feature Extraction Labelling Model with Priority Weighted Feature Selection (DWLM-PWFS). This approach combines the tumor's intensity, texture, shape and diagnostic properties. The selection of features with the technique proposed is most helpful for analyzing data according to grouping class variable and ensuring reduced feature setting with high classification accuracy. In contrast to the conventional model, the model proposed is shown to be highly efficient in comparison with traditional models.

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Brain Tumor Diagnosis Support System: A Decision Fusion Framework

Cited by 2 publications

References 13 publications

Deep Learning Feature Extraction for Brain Tumor Characterization and Detection

Deep Learning Feature Extraction for Brain Tumor Characterization and Detection

Accurate Brain Tumor Recognition Using Double-Weighted Feature Extraction Labelling Model with Priority Weighted Feature Selection

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