Brain tumor classification using modified kernel based softplus extreme learning machine

Sasank, V. V. S.; Venkateswarlu, S.

doi:10.1007/s11042-020-10423-5

Cited by 24 publications

(4 citation statements)

References 29 publications

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“…Utilizing a KSELM classifier, the images are classified as normal or abnormal based on the features that were chosen ( Sasank & Venkateswarlu, 2021 ). The input images are initially in the size of 512×512 pixels and they are resized to 256×256×3 to make them compatible with the KSELM classifier.…”

Section: Proposed Methodologymentioning

confidence: 99%

ACO-KELM: Anti Coronavirus Optimized Kernel-based Softplus Extreme Learning Machine for classification of skin cancer

Liu

Rejeesh²,

Sundararaj³

et al. 2023

Expert Systems with Applications

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Section: Proposed Methodologymentioning

confidence: 99%

ACO-KELM: Anti Coronavirus Optimized Kernel-based Softplus Extreme Learning Machine for classification of skin cancer

Liu

Rejeesh²,

Sundararaj³

et al. 2023

Expert Systems with Applications

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“…Likewise, a new framework has been created for classifying brain cancers. To extract the features, the suggested model includes six layers [ 44 , 45 ]. A multiclass CNN model has been developed for tumor classification.…”

Section: Related Workmentioning

confidence: 99%

A New Model for Brain Tumor Detection Using Ensemble Transfer Learning and Quantum Variational Classifier

Amin

Anjum

Sharif

et al. 2022

Computational Intelligence and Neuroscience

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A brain tumor is an abnormal enlargement of cells if not properly diagnosed. Early detection of a brain tumor is critical for clinical practice and survival rates. Brain tumors arise in a variety of shapes, sizes, and features, with variable treatment options. Manual detection of tumors is difficult, time-consuming, and error-prone. Therefore, a significant requirement for computerized diagnostics systems for accurate brain tumor detection is present. In this research, deep features are extracted from the inceptionv3 model, in which score vector is acquired from softmax and supplied to the quantum variational classifier (QVR) for discrimination between glioma, meningioma, no tumor, and pituitary tumor. The classified tumor images have been passed to the proposed Seg-network where the actual infected region is segmented to analyze the tumor severity level. The outcomes of the reported research have been evaluated on three benchmark datasets such as Kaggle, 2020-BRATS, and local collected images. The model achieved greater than 90% detection scores to prove the proposed model's effectiveness.

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“…Henceforth, it is very essential to segment and classify two different kinds of tumor from MRI so that, doctors can easily provide a correct treatment to the patient. In this research [19] , V.V. Sasank and S. Venkateswarlu developed a machine learning based classification in brain MRI.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybridized Deep Neural Network Using Adaptive Rain Optimizer Algorithm for Multi-Grade Brain Tumor Classification of MRI Images

Sasank

Venkateswarlu

2022

digit. tech. res. and appl.

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Classification of brain tumor is highly significant in the medical field in real-world to improve the progress of treatments. The seriousness behind the tumors are normally graded based on the size into grade I, grade II, grade III and grade IV. This is where the process of multi-grade brain tumor classification gains attention. Thus, the article focusses on classifying the brain MRI images into four different grades by proposing a novel and a very efficient classification strategy with high accuracy. The acquired images are pre-processed with the help of an Extended Adaptive Wiener Filter (EAWF) and then segmented using the piecewise Fuzzy C- means Clustering (piFCM) technique. Then the most ideal features such as the texture, intensity and shape features that can best explain the growth of tumors are extracted using the Local Binary Pattern (LBP) and the Hybrid Local Directional Pattern with Gabor Filter (HLDP-GF) techniques. After extracting the ideal features, the Manta Ray Foraging Optimization (MRFO) method has been introduced to optimally select the most relevant features. Finally, a Hybrid Deep Neural Network with Adaptive Rain Optimizer Algorithm (HDNN- AROA) is proposed to classify the grades of brain tumors with high accuracy and efficiency. The proposed technique has been compared with the existing state-of-the-art techniques relevant to brain tumor classification in terms of accuracy, precision, recall and dice similarity coefficient to prove the overall efficiency of the system.

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Brain tumor classification using modified kernel based softplus extreme learning machine

Cited by 24 publications

References 29 publications

ACO-KELM: Anti Coronavirus Optimized Kernel-based Softplus Extreme Learning Machine for classification of skin cancer

ACO-KELM: Anti Coronavirus Optimized Kernel-based Softplus Extreme Learning Machine for classification of skin cancer

A New Model for Brain Tumor Detection Using Ensemble Transfer Learning and Quantum Variational Classifier

Hybridized Deep Neural Network Using Adaptive Rain Optimizer Algorithm for Multi-Grade Brain Tumor Classification of MRI Images

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