Optimization driven Deep Convolution Neural Network for brain tumor classification

Kumar, Sharan; Mankame, Dattatreya P.

doi:10.1016/j.bbe.2020.05.009

Cited by 122 publications

(51 citation statements)

References 18 publications

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“…In [ 85 ], the authors propose a Deep CNN based on the Dolphin Echolocation-based Sine Cosine Algorithm. The algorithm uses Dolphin-SCA-based fuzzy fusion model for segmentation, and then, the statistical attributes of weight, mean value, variance, and skewness are used for feature calculation.…”

Section: Methods Analysismentioning

confidence: 99%

A State-of-the-Art Review for Gastric Histopathology Image Analysis Approaches and Future Development

et al. 2021

BioMed Research International

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Gastric cancer is a common and deadly cancer in the world. The gold standard for the detection of gastric cancer is the histological examination by pathologists, where Gastric Histopathological Image Analysis (GHIA) contributes significant diagnostic information. The histopathological images of gastric cancer contain sufficient characterization information, which plays a crucial role in the diagnosis and treatment of gastric cancer. In order to improve the accuracy and objectivity of GHIA, Computer-Aided Diagnosis (CAD) has been widely used in histological image analysis of gastric cancer. In this review, the CAD technique on pathological images of gastric cancer is summarized. Firstly, the paper summarizes the image preprocessing methods, then introduces the methods of feature extraction, and then generalizes the existing segmentation and classification techniques. Finally, these techniques are systematically introduced and analyzed for the convenience of future researchers.

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Section: Methods Analysismentioning

confidence: 99%

A State-of-the-Art Review for Gastric Histopathology Image Analysis Approaches and Future Development

et al. 2021

BioMed Research International

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“…The authors of [27] improved the classification accuracy by applying the Dolphin-SCA deep learning mechanism to enhance the accuracy and yield better results. Fuzzy deformable fusion modeling is employed to carry out the segmentation procedure using Dolphin Echolocation-based Sine Cosine Algorithm.…”

Section: Hybrid Techniquesmentioning

confidence: 99%

Segmenting and Classifiying the Brain Tumor from MRI Medical Images Based on Machine Learning Algorithms: A Review

Kareem

Al-Sulaifanie

Hasan

et al. 2021

AJRCoS

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A brain tumor is a problem that threatens life and impedes the normal working of the human body. The brain tumor needs to be identified early for the proper diagnosis and effective treatment planning. Tumor segmentation from an MRI brain image is one of the most focused areas of the medical community, provided that MRI is non-invasive imaging. Brain tumor segmentation involves distinguishing abnormal brain tissue from normal brain tissue. This paper presents a systematic literature review of brain tumor segmentation strategies and the classification of abnormalities and normality in MRI images based on various deep learning techniques, interbreeding. It requires presentation and quantitative analysis, from standard segmentation and classification methods to the best class strategies.

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“…Deep learning (DL) is a branch of machine learning based on a system development to study multiple level representation by creating hierarchical features, where higher level is determined from lower level and similar lower level features could assist in determining numerous higher level features [11]. Convolutional Neural Network is an algorithm in deep learning that focuses on the problem of classifying an image data [12]. Hence, CNN method has the ability to study, represent, and classify objects.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network with Hyperparameter Tuning for Brain Tumor Classification

Minarno

Mandiri

Munarko

et al. 2021

KINETIK

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Brain tumor has been acknowledged as the most dangerous disease through all its circles. Early identification of tumor disease is considered pivotal to identify the spread of brain tumors in administering the appropriate treatment. This study proposes a Convolutional Neural Network method to detect brain tumor on MRI images. The 3264 datasets were undertaken in this study with detailed images of Glioma tumor (926 images), Meningioma tumors (937 images), pituitary tumors (901 images), and other with no-tumors (500 images). The application of CNN method combined with Hyperparameter Tuning is proposed to achieve optimal results in classifying the brain tumor types. Hyperparameter Tuning acts as a navigator to achieve the best parameters in the proposed CNN model. In this study, the model testing was conducted with three different scenarios. The result of brain tumor classification depicts an accuracy of 96% in the third model testing scenario.

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Optimization driven Deep Convolution Neural Network for brain tumor classification

Cited by 122 publications

References 18 publications

A State-of-the-Art Review for Gastric Histopathology Image Analysis Approaches and Future Development

A State-of-the-Art Review for Gastric Histopathology Image Analysis Approaches and Future Development

Segmenting and Classifiying the Brain Tumor from MRI Medical Images Based on Machine Learning Algorithms: A Review

Convolutional Neural Network with Hyperparameter Tuning for Brain Tumor Classification

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