Brain Tumor Classification Using Deep Neural Network

Çınarer, Gökalp; Arslan, Recep Sinan; Yurttakal, Ahmet Haşim

doi:10.25046/aj050593

Cited by 14 publications

(3 citation statements)

References 31 publications

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“…Following feature extraction and data reduction, the classification layer produces outputs corresponding to the number of objects targeted for classification. This sequential process forms the foundation of CNN-based image recognition systems (Çınarer et al, 2020;Güler & Yücedağ, 2022).…”

Section: Methodsmentioning

confidence: 99%

A Weighted Ensemble Approach with Multiple Pre-trained Deep Learning Models for Classification of Stroke

Alhatemi,

Savaş

2023

MEDIN

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Stroke ranks as one of the deadliest diseases globally, emphasizing the crucial need for early diagnosis. This study aims to create a two-stage classification system for stroke and non-stroke images to support early clinical detection. Deep learning, a cornerstone of diagnosis, detection, and prompt treatment, is the primary methodology. Transfer learning adapts successful deep learning architectures for diverse problems, and ensemble learning combines multiple classifiers for enhanced results. These two techniques are applied to classify stroke using a dataset of stroke and normal images. In the initial stage, six pre-trained models are fine-tuned, with DenseNet, Xception, and EfficientNetB2 emerging as the top performers, achieving validation accuracies of 98.4%, 98.4%, and 98%, respectively. These models serve as base learners within an ensemble framework. A weighted average ensemble method combines them, resulting in a remarkable 99.84% accuracy on a reserved test dataset. This approach exhibits promise for stroke detection, a life-threatening condition, while also demonstrating the effectiveness of ensemble techniques in enhancing model performance.

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Section: Methodsmentioning

confidence: 99%

A Weighted Ensemble Approach with Multiple Pre-trained Deep Learning Models for Classification of Stroke

Alhatemi,

Savaş

2023

MEDIN

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“…Moving on to the next layer, a flattening operation is employed to convert the feature maps into a vector form. During the classification phase, this flattened input vector is passed through the network to generate numerical outputs in each output neuron [47]. Essentially, this process transforms the feature representation into a single extended layer that originates from the convolutional layer.…”

Section: Feature Learnermentioning

confidence: 99%

Enhancing Phishing URL Detection Accuracy in Software-Defined Networks (SDNs) through Feature Selection and Machine Learning Techniques

RUBY,

2024

Preprint

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Phishing attacks remain an enduring and ever-evolving menace to both networked systems and their users' privacy. In response to this formidable challenge, our research delves into an innovative approach designed to enhance the precision of phishing Uniform Resource Locator (URL) detection within the dynamic and programmable realm of Software-Defined Networks (SDNs). By harnessing feature selection capabilities and adaptive machine learning techniques, our proposed framework aims to fortify security measures in SDNs against these malicious campaigns. Our methodology's core is the deliberate selection of discriminative features from the extensive network data attributes. This feature selection process is meticulously designed to identify the most relevant characteristics associated with phishing URLs, thereby enabling the extraction of invaluable insights for more precise detection. These carefully chosen features then serve as inputs for a dynamic machine learning model, trained to adapt and evolve alongside the constantly changing landscape of phishing attacks. Within the SDN environment, our framework optimizes utilizing network resources and controller processing power. It achieves this by reducing the dimensionality of input data, resulting in improved detection accuracy and a decrease in false positives. The adaptive nature of our machine learning model ensures rapid recognition of emerging phishing tactics, thereby reducing the risk of succumbing to novel and sophisticated attacks. To validate the effectiveness of our approach, we conducted extensive experiments and evaluations within an SDN testbed, utilizing real-world phishing URL datasets. The results consistently demonstrate that our framework surpasses conventional methods, achieving higher detection accuracy and adaptability to evolving threats. In summary, our research represents a significant stride in the ongoing battle against phishing attacks by leveraging the dynamic capabilities of SDNs. The synergy between feature selection and adaptive machine learning techniques empowers SDNs to sustain accurate and effective phishing URL detection, ultimately reinforcing network security and safeguarding user privacy in an ever-evolving threat landscape.

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“…Deep CNN can extract multiple levels of features from image-based data and reveals distinguishing features between classes for classification problems [16,17]. While most studies focus on MRI or Computed Tomography (CT) images, histopathological images have a high potential in the background.…”

Section: A Brain Glioma Classificationmentioning

confidence: 99%

Comparison Review on Brain Tumor Classification and Segmentation using Convolutional Neural Network (CNN) and Capsule Network

Ali¹,

Mokri²,

Halim³

et al. 2023

IJACSA

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Malignant brain glioma is considered as one of the deadliest cancer diseases that has a higher fatality rate than the survival rate. In terms of brain glioma imaging and diagnosis, the processes of detection and segmentation are manually done by the experts. However, with the advancement of artificial intelligence, the implementation of these tasks using deep learning provides an efficient solution to the management of brain glioma diagnosis and patient treatment. Deep learning networks are responsible for detecting, segmenting, and interpreting the tumors with high accuracy and repeatability so that the appropriate treatment planning can be offered to the patient. This paper presents a comparison review between two state of the art deep learning networks namely convolutional neural network and capsule network in performing brain glioma classification and segmentation tasks. The performance of each published method is discussed along with their advantages and disadvantages. Next, the related constraints in both networks are outlined and highlighted for future research.

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Brain Tumor Classification Using Deep Neural Network

Cited by 14 publications

References 31 publications

A Weighted Ensemble Approach with Multiple Pre-trained Deep Learning Models for Classification of Stroke

A Weighted Ensemble Approach with Multiple Pre-trained Deep Learning Models for Classification of Stroke

Enhancing Phishing URL Detection Accuracy in Software-Defined Networks (SDNs) through Feature Selection and Machine Learning Techniques

Comparison Review on Brain Tumor Classification and Segmentation using Convolutional Neural Network (CNN) and Capsule Network

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