Internet of medical things and cloud enabled brain tumour diagnosis model using deep learning with kernel extreme learning machine

Ganesan, M.; Sivakumar, N.; Thirumaran, M.; Vengattaraman, T.

doi:10.1504/ijeh.2022.124490

Cited by 3 publications

(2 citation statements)

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“…An adaptive layer cascaded ResNet (ALCResNet) optimized with improved border collie optimization (IBCO) to detect and classify brain tumours [35]. Brain tumour diagnosis and classification using a deep learning-based inception model with the kernel extreme learning machine was proposed [36]. The ANFIS-based brain tumour detection and classification [37,38] have required more speed and accuracy.…”

Section: Related Workmentioning

confidence: 99%

An adaptive neuro‐fuzzy inference system optimized by genetic algorithm for brain tumour detection in magnetic resonance images

Ghahramani,

Shiri

2024

IET Image Processing

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An adaptive neuro‐fuzzy inference system is presented which is optimized by a genetic algorithm to classify normal and abnormal brain tumours. The classifier is fast and simple, named genetic algorithm‐adaptive neuro‐fuzzy inference system, and the determined learning rules minimize its error and improve its accuracy. The presented system follows five steps including preprocessing, morphological operation, feature extraction, feature selection, and classification. Morphological operators segment the abnormal regions and calculate the tumour area. The statistical features and the grey‐level co‐occurrence matrix are employed for feature extraction. Magnetic resonance images are considered and 12 statistical features are extracted, then the genetic algorithm‐based selection technique helps to select features and reduce the extracted features and improves the accuracy and decision time. So, the high dimensionality and the computational complexity of the adaptive neuro‐fuzzy inference system are reduced, and the classifier decides more efficiently. The input data are the figshare brain tumour dataset with 670 abnormal and 670 normal magnetic resonance images, and the classifier requires 10.788 s for classification. The efficient performance of the genetic algorithm‐adaptive neuro‐fuzzy inference system is confirmed by the accuracy of 99.85%, sensitivity of 99.7%, specificity of 100%, precision of 100%, and mean square error of 0.0027.

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Section: Related Workmentioning

confidence: 99%

An adaptive neuro‐fuzzy inference system optimized by genetic algorithm for brain tumour detection in magnetic resonance images

Ghahramani,

Shiri

2024

IET Image Processing

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“…A 48-layer pre-trained convolutional neural network called Inception v3 has previously been trained on more than a million photos from the ImageNet collection [22]. This pretrained network can categorise photos into 1000 different object categories, including a variety of animals, a keyboard, a mouse, and a pencil.…”

Section: Inception V3 Approachmentioning

confidence: 99%

Comparison of Transfer Learning Techniques to Classify Brain Tumours Using MRI Images

Jain,

Kubadia,

Mangla

et al. 2024

RAiSE-2023

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SBTC-Net: Secured Brain Tumor Segmentation and Classification Using Black Widow With Genetic Optimization in IoMT

Ramprasad,

Rahman,

Bayleyegn

2023

IEEE Access

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Internet of medical things and cloud enabled brain tumour diagnosis model using deep learning with kernel extreme learning machine

Cited by 3 publications

References 0 publications

An adaptive neuro‐fuzzy inference system optimized by genetic algorithm for brain tumour detection in magnetic resonance images

An adaptive neuro‐fuzzy inference system optimized by genetic algorithm for brain tumour detection in magnetic resonance images

Comparison of Transfer Learning Techniques to Classify Brain Tumours Using MRI Images

SBTC-Net: Secured Brain Tumor Segmentation and Classification Using Black Widow With Genetic Optimization in IoMT

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