Brain Tumor Classification Using Convolutional Neural Network

Abiwinanda, Nyoman; Hanif, Muhammad Abdullah; Hesaputra, S. Tafwida; Handayani, Astri; Mengko, Tati L. R.

doi:10.1007/978-981-10-9035-6_33

Cited by 313 publications

(149 citation statements)

References 12 publications

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“…The performance of the ARKFCM-HFE-DNN method was implemented in terms of specificity, accuracy, sensitivity, and Fmeasure. To evaluate the classification efficiency of the ARKFCM-HFE-DNN method, the performance of the proposed method is compared with conventional methods: CNN and SVM on reputed dataset: T1-WCEMRI [19,20].…”

Section: Resultsmentioning

confidence: 99%

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Genetic Algorithm Based Feature Selection Brain Tumour Segmentation and Classification

Kumar¹,

Krishna²,

Kusumavathi³

2019

IJIES

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Brain tumour segmentation is a significant task in the Medical Image Processing (MIP). Early diagnosis of the brain tumours plays an important role in improving treatment possibilities and maximizes the survival rate of the patients. Manual segmentation of the brain tumours for the cancer diagnosis form large amount of MRI images that generated in clinical routine, its complex and time consuming task. So, the automatic brain tumour image segmentation process is required. In this research, a tumour portion is segmented from the brain image by using Adaptively Regularized Kernel-Based Fuzzy C-Means (ARKFCM) algorithm. The input images are resized like 256×256 in the pre -processing stage. The pre-processed MRI image segmented by ARKFCM, which is a flexible high-level Machine Learning (ML) technique to locate the object in a complex template. Next, Hybrid Feature Extraction (HFE) performed on the segmented image to improve the feature subsets. The feature selection process was performed by Kernel Nearest Neighbour (KNN) based Genetic Algorithm (GA) in order to acquire the best feature values. The best feature values given to the Deep Neural Network (DNN) classifier as an input, which is classified into Meningioma, Glioma and Pituitary regions in the MRI images. The performance of proposed ARKFCM -HFE -DNN method is validated by T1-WCEMRI dataset. The experimental outcome showed that the ARKFCM -HFE -DNN method improved the average classification accuracy up to 6.19 % than existing classification techniques such as Convolution Neural Network (CNN) and Support Vector Machine (SVM).

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Section: Resultsmentioning

confidence: 99%

“…N. Abiwinanda, M. Hanif, S.T. Hesaputra, A. Handayani, and T. R. Mengko [19] proposed a brain tumour classification using CNN. They classified the brain tumours into three types: Glioma, Meningioma, and Pituitary by using CNN.…”

Section: Literature Surveymentioning

confidence: 99%

Genetic Algorithm Based Feature Selection Brain Tumour Segmentation and Classification

Kumar¹,

Krishna²,

Kusumavathi³

2019

IJIES

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“…Therefore, what is defined as normal for one subject can be abnormal for another, requiring learning of abnormal and normal scenarios in order to discriminate. Hence most approaches in the medical domain have used supervised learning [13,36,37,38]. With the recent spectacular success of deep learning methods for automatically learning task specific features, hand engineered features have been replaced by deep learned features for medical anomaly detection.…”

Section: Anomaly Detectionmentioning

confidence: 99%

Neural memory plasticity for medical anomaly detection

et al. 2020

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“…They extracted four features (Energy, Correlation, Contrast, and Homogeneity) from four angles (0°, 45°, 90°, and 135°) for each image and then these features are fed into CNN, they tested their methodology on four different datasets (Mg-Gl, Mg-Pt, Gl-Pt, and Mg-Gl-Pt) and the best accuracy achieved was82.27% for Gl-Pt dataset using two sets of features; contrast with homogeneity and contrast with correlation. Seetha, J., and S. S. Raja [12] proposed a deep CNN based system for automated brain tumor detection and grading. The system is based on Fuzzy C-Means (FCM) for brain segmentation and based on these segmented regions a texture and shape features were extracted then these features were fed into SVM and DNN classifiers.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Such systems can help physicians to increase the accuracy of cancer early detection. Recently, many artificial intelligence techniques such as an artificial neural network (ANN), support vector machine (SVM), and convolutional neural network (CNN) have been applied to classify and recognize brain tumors [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Brain Tumor Classification Using Deep Learning Technique - A Comparison between Cropped, Uncropped, and Segmented Lesion Images with Different Sizes

Alqudah¹,

Alquraan²,

Abu-Qasmieh³

et al. 2019

IJATCSE

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Deep Learning is the newest and the current trend of the machine learning field that paid a lot of the researchers' attention in the recent few years. As a proven powerful machine learning tool, deep learning was widely used in several applications for solving various complex problems that require extremely high accuracy and sensitivity, particularly in the medical field. In general, the brain tumor is one of the most common and aggressive malignant tumor diseases which is leading to a very short expected life if it is diagnosed at a higher grade. Based on that, brain tumor grading is a very critical step after detecting the tumor in order to achieve an effective treating plan. In this paper, we used Convolutional Neural Network (CNN) which is one of the most widely used deep learning architectures for classifying a dataset of 3064 T1 weighted contrast-enhanced brain MR images for grading (classifying) the brain tumors into three classes (Glioma, Meningioma, and Pituitary Tumor). The proposed CNN classifier is a powerful tool and its overall performance with an accuracy of 98.93% and sensitivity of 98.18% for the cropped lesions, while the results for the uncropped lesions are 99% accuracy and 98.52% sensitivity and the results for segmented lesion images are 97.62% for accuracy and 97.40% sensitivity.

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Brain Tumor Classification Using Convolutional Neural Network

Cited by 313 publications

References 12 publications

Genetic Algorithm Based Feature Selection Brain Tumour Segmentation and Classification

Genetic Algorithm Based Feature Selection Brain Tumour Segmentation and Classification

Neural memory plasticity for medical anomaly detection

Brain Tumor Classification Using Deep Learning Technique - A Comparison between Cropped, Uncropped, and Segmented Lesion Images with Different Sizes

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