Automatic detection of brain tumors with the aid of ensemble deep learning architectures and class activation map indicators by employing magnetic resonance images

Türk, Ömer; Özhan, Davut; Acar, Emrullah; Akıncı, Tahir Çetin; Yılmaz, Musa

doi:10.1016/j.zemedi.2022.11.010

Cited by 12 publications

(13 citation statements)

References 22 publications

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“…There are four parameters to evaluate the performance of the models. These; F-1 score, recall, accuracy and precision [13][14]. Test results and performance metrics of the CNN and ResNet50 models are as in Table I.…”

Section: ) Resnet50mentioning

confidence: 99%

Detection of Various Diseases in Fruits and Vegetables with the Help of Different Deep Learning Techniques

Acar,

Özcan

2024

Balkan Journal of Electrical and Computer Engineering

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Fruit and vegetable diseases have an important place in the food sector in terms of sustainable agricultural policies. Thus, ıt affects tissues, targeting and negatively impacting the food supply. In this study, Two separate Deep Learning (CNN, AlexNet) models were employed to detect this difference, visual damage and surface marker seen in fruits and vegetables. 22 strawberries and 18 tomato images were used for this analysis, and than data augmentation was implemented 600 images out of 40 images using the image reproduction broadcast. As a result, 83.3% success was achieved.

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Section: ) Resnet50mentioning

confidence: 99%

Detection of Various Diseases in Fruits and Vegetables with the Help of Different Deep Learning Techniques

Acar,

Özcan

2024

Balkan Journal of Electrical and Computer Engineering

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“…The input data were set at the input vector, and then RєI, rєR so r(t). It was the input vector at time t and s(t) 1 i is the raw vector at each input i. When the unit was closer to the winning neuron, it was defined as the best-winning neuron.…”

Section: Clusteringmentioning

confidence: 99%

“…An MRI scan automation system using ML was presented in research [ 1 ] for detecting brain cancers. The suggested system went through three phases of implementation.…”

Section: Related Workmentioning

confidence: 99%

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An Efficient Brain Tumor Segmentation Method Based on Adaptive Moving Self-Organizing Map and Fuzzy K-Mean Clustering

Dalal,

Lilhore,

Manoharan

et al. 2023

Sensors

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Brain tumors in Magnetic resonance image segmentation is challenging research. With the advent of a new era and research into machine learning, tumor detection and segmentation generated significant interest in the research world. This research presents an efficient tumor detection and segmentation technique using an adaptive moving self-organizing map and Fuzzyk-mean clustering (AMSOM-FKM). The proposed method mainly focused on tumor segmentation using extraction of the tumor region. AMSOM is an artificial neural technique whose training is unsupervised. This research utilized the online Kaggle Brats-18 brain tumor dataset. This dataset consisted of 1691 images. The dataset was partitioned into 70% training, 20% testing, and 10% validation. The proposed model was based on various phases: (a) removal of noise, (b) selection of feature attributes, (c) image classification, and (d) tumor segmentation. At first, the MR images were normalized using the Wiener filtering method, and the Gray level co-occurrences matrix (GLCM) was used to extract the relevant feature attributes. The tumor images were separated from non-tumor images using the AMSOM classification approach. At last, the FKM was used to distinguish the tumor region from the surrounding tissue. The proposed AMSOM-FKM technique and existing methods, i.e., Fuzzy-C-means and K-mean (FMFCM), hybrid self-organization mapping-FKM, were implemented over MATLAB and compared based on comparison parameters, i.e., sensitivity, precision, accuracy, and similarity index values. The proposed technique achieved more than 10% better results than existing methods.

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“…This model consumed lower time in training and computing. O. Turk, et.al (2022) projected a method in which ensemble deep learning (DL) methods known as ResNet50, InceptionV3, MobileNet and VGG19 were exploited for the purpose of diagnosing the brain tumors in an automatic way [16]. Moreover, Class Activation Maps (CAMs) indicators were also employed based on Magnetic Resonance Imaging (MRI) images [16].…”

Section: IImentioning

confidence: 99%

Analysis of Brain Tumor Detection Techniques

Das,

Jangde,

Bhattacharya

2023

IJRASET

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The bio-medical image processing is the application of image processing to process medical data. The techniques of feature extraction and machine learning are efficient techniques brain tumor detection. Various network architectures are utilized, ordinarily with hundreds or thousands of adjustable parameters. The resulting condition forms are sufficiently general to unravel a large class of nonlinear classification and estimation problems and sufficiently complex to hide a multitude of sins. In this paper, various brain tumor detection techniques are reviewed analyzed in terms of certain parameters. It is analyzed that textual feature extraction and classification techniques give maximum accuracy for the brain tumor detection

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Automatic detection of brain tumors with the aid of ensemble deep learning architectures and class activation map indicators by employing magnetic resonance images

Cited by 12 publications

References 22 publications

Detection of Various Diseases in Fruits and Vegetables with the Help of Different Deep Learning Techniques

Detection of Various Diseases in Fruits and Vegetables with the Help of Different Deep Learning Techniques

An Efficient Brain Tumor Segmentation Method Based on Adaptive Moving Self-Organizing Map and Fuzzy K-Mean Clustering

Analysis of Brain Tumor Detection Techniques

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