A deep neural network based classifier for brain tumor diagnosis

Kumar, Ambeshwar; Ramachandran, Manikandan; Gandomi, Amir H.; Patan, Rizwan; Łukasik, Szymon; Soundarapandian, Ravichandran Kattur

doi:10.1016/j.asoc.2019.105528

Cited by 32 publications

(19 citation statements)

References 16 publications

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“…Early brain diagnosis and treatment are found to be paramount to avoid damage to the patient. Reference [5] described an approach for minimizing misclassification error called Weighted Correlation Feature Selection Based Iterative Bayesian Multivariate Deep Neural Learning (WCFSIBMDNL). By using the WCFSIBMDNL approach, it is possible to overcome the complexity issue associated with lung tumors in their convoluted stage.…”

Section: Related Workmentioning

confidence: 99%

“…From equation (5), by minimizing the objective function (i.e., minimizing error) with maximum accuracy "MIN MAX" using a generator function "GOF" for corresponding subset of features "F," higher rate of disease diagnosis is said to be achieved. is is performed by applying the expectation "P" and corresponding generator being "O" with the expectation equivalent to probability distribution and generator function.…”

Section: Generator Deep Learning Modelmentioning

confidence: 99%

“…. , bi n 􏼈 􏼉" Output: Improved diagnosis accuracy (1) Initialize Weight "Wi" and Bias "Bi" (2) Begin (3) For each subset of features "F n " (4) Obtain generator function (5) Obtain minmax for generator function for subset of feature ( 6) Return (probability rate) (7) End for (8) End ALGORITHM 2: Generator Deep Learning. 8 Journal of Healthcare Engineering this, it is inferred that the time complexity is reduced using WS-GDL method.…”

Section: Performance Evaluation Of Computational Complexitymentioning

confidence: 99%

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Adaptive Diagnosis of Lung Cancer by Deep Learning Classification Using Wilcoxon Gain and Generator

Obulesu¹,

Kalyanasundaram²,

Dhiman³

et al. 2021

Journal of Healthcare Engineering

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Cancer is a complicated worldwide health issue with an increasing death rate in recent years. With the swift blooming of the high throughput technology and several machine learning methods that have unfolded in recent years, progress in cancer disease diagnosis has been made based on subset features, providing awareness of the efficient and precise disease diagnosis. Hence, progressive machine learning techniques that can, fortunately, differentiate lung cancer patients from healthy persons are of great concern. This paper proposes a novel Wilcoxon Signed-Rank Gain Preprocessing combined with Generative Deep Learning called Wilcoxon Signed Generative Deep Learning (WS-GDL) method for lung cancer disease diagnosis. Firstly, test significance analysis and information gain eliminate redundant and irrelevant attributes and extract many informative and significant attributes. Then, using a generator function, the Generative Deep Learning method is used to learn the deep features. Finally, a minimax game (i.e., minimizing error with maximum accuracy) is proposed to diagnose the disease. Numerical experiments on the Thoracic Surgery Data Set are used to test the WS-GDL method's disease diagnosis performance. The WS-GDL approach may create relevant and significant attributes and adaptively diagnose the disease by selecting optimal learning model parameters. Quantitative experimental results show that the WS-GDL method achieves better diagnosis performance and higher computing efficiency in computational time, computational complexity, and false-positive rate compared to state-of-the-art approaches.

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Section: Related Workmentioning

confidence: 99%

Section: Generator Deep Learning Modelmentioning

confidence: 99%

Section: Performance Evaluation Of Computational Complexitymentioning

confidence: 99%

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Adaptive Diagnosis of Lung Cancer by Deep Learning Classification Using Wilcoxon Gain and Generator

Obulesu¹,

Kalyanasundaram²,

Dhiman³

et al. 2021

Journal of Healthcare Engineering

Self Cite

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“…al. [8] have developed a method which consist of feature selection by using weighted correlation and multivariate deep neural networks for early detection of brain tumor.…”

Section: Introductionmentioning

confidence: 99%

“…al. [8], which considers a brain image dataset for tumor classification at a beginning period. The system does a Feature Selection based on Weighted Correlation by picking therapeutic element subsets that are applicable for grouping brain tumors.…”

Section: Introductionmentioning

confidence: 99%

A BrainNet Classification Technique Based on Deep Convolutional Neural Network for Detection of Brain Tumor in FLAIR MRI Images

Manoj*¹,

Gunasekaran²,

Jaisingh³

2019

IJEAT

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Classification process plays a key role in diagnosing brain tumors. Earlier research works are intended for identifying brain tumors using different classification techniques. The logical gap between the visual representation of data captured by MRI device and the information apparent to the person evaluating poses a key challenge in the medical field. Research in computerized segmentation of tumor is widely gaining popularity nowadays, which may lead to an accurate analysis of MRI images and planned treatment of patients. The recent field of deep learning and neural networks promises to classify images with higher accuracy. This work proposes a new BrainNet classification technique that combines fuzzy c means, morphological operators and CNN to identify image regions that are suspicious. The proposed method is assessed with the help of imaging data obtained from Multimodal Brain Tumor Image Segmentation Challenge (BRATS) 2015 and IXI dataset. The effectiveness of the proposed method is computed with traditional machine learning and Convolutional Neural Networks. Experimental results show that our proposed method outperforms state-of-the-art classification on the BRATS 2015 dataset.

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Classification Methods and Its Applications in Infrastructure Management

2022

Automation and Computational Intelligence for Road Maintenance and Management

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A deep neural network based classifier for brain tumor diagnosis

Cited by 32 publications

References 16 publications

Adaptive Diagnosis of Lung Cancer by Deep Learning Classification Using Wilcoxon Gain and Generator

Adaptive Diagnosis of Lung Cancer by Deep Learning Classification Using Wilcoxon Gain and Generator

A BrainNet Classification Technique Based on Deep Convolutional Neural Network for Detection of Brain Tumor in FLAIR MRI Images

Classification Methods and Its Applications in Infrastructure Management

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