Brain Tumor Segmentation Using OTSU Embedded Adaptive Particle Swarm Optimization Method and Convolutional Neural Network

Vijh, Surbhi; Sharma, Shilpi; Gaurav, Prashant

doi:10.1007/978-3-030-25797-2_8

Cited by 50 publications

(33 citation statements)

References 58 publications

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“…In addition, classification has been carried out on other image databases, which are also quite small [15][16][17][18]. Mohsen et al used 66 images to classify four types of images showing brain tumors: tumor-free, glioblastoma, sarcoma, and metastasis.…”

Section: Introductionmentioning

confidence: 99%

Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network

2020

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The classification of brain tumors is performed by biopsy, which is not usually conducted before definitive brain surgery. The improvement of technology and machine learning can help radiologists in tumor diagnostics without invasive measures. A machine-learning algorithm that has achieved substantial results in image segmentation and classification is the convolutional neural network (CNN). We present a new CNN architecture for brain tumor classification of three tumor types. The developed network is simpler than already-existing pre-trained networks, and it was tested on T1-weighted contrast-enhanced magnetic resonance images. The performance of the network was evaluated using four approaches: combinations of two 10-fold cross-validation methods and two databases. The generalization capability of the network was tested with one of the 10-fold methods, subject-wise cross-validation, and the improvement was tested by using an augmented image database. The best result for the 10-fold cross-validation method was obtained for the record-wise cross-validation for the augmented data set, and, in that case, the accuracy was 96.56%. With good generalization capability and good execution speed, the new developed CNN architecture could be used as an effective decision-support tool for radiologists in medical diagnostics.

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Section: Introductionmentioning

confidence: 99%

Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network

2020

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“…Vijh et al [ 26 ] presented an adaptive particle swarm optimization (PSO) with the Otsu method to find the optimal threshold value. Later, they applied anisotropic diffusion (AD) filtering on brain MRI images to cancel noise and improve image quality.…”

Section: Related Workmentioning

confidence: 99%

Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists

et al. 2020

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Manual identification of brain tumors is an error-prone and tedious process for radiologists; therefore, it is crucial to adopt an automated system. The binary classification process, such as malignant or benign is relatively trivial; whereas, the multimodal brain tumors classification (T1, T2, T1CE, and Flair) is a challenging task for radiologists. Here, we present an automated multimodal classification method using deep learning for brain tumor type classification. The proposed method consists of five core steps. In the first step, the linear contrast stretching is employed using edge-based histogram equalization and discrete cosine transform (DCT). In the second step, deep learning feature extraction is performed. By utilizing transfer learning, two pre-trained convolutional neural network (CNN) models, namely VGG16 and VGG19, were used for feature extraction. In the third step, a correntropy-based joint learning approach was implemented along with the extreme learning machine (ELM) for the selection of best features. In the fourth step, the partial least square (PLS)-based robust covariant features were fused in one matrix. The combined matrix was fed to ELM for final classification. The proposed method was validated on the BraTS datasets and an accuracy of 97.8%, 96.9%, 92.5% for BraTs2015, BraTs2017, and BraTs2018, respectively, was achieved.

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“…A novel brain tumor segmentation architecture was proposed by Vijh et al [ 71 ] that employs a blend of Otsu thresholding, Adaptive Particle Swarm Optimization (APSO), and morphological operations in the skull stripping preprocessing steps. Skull stripping removes noncerebral tissue not needed for analysis and is a crucial step in neurological imaging.…”

Section: Dcnns Application In the Segmentation Of Brain Cancer Imamentioning

confidence: 99%

The Application of Deep Convolutional Neural Networks to Brain Cancer Images: A Survey

Shirazi

Fornaciari

McDonnell

et al. 2020

JPM

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In recent years, improved deep learning techniques have been applied to biomedical image processing for the classification and segmentation of different tumors based on magnetic resonance imaging (MRI) and histopathological imaging (H&E) clinical information. Deep Convolutional Neural Networks (DCNNs) architectures include tens to hundreds of processing layers that can extract multiple levels of features in image-based data, which would be otherwise very difficult and time-consuming to be recognized and extracted by experts for classification of tumors into different tumor types, as well as segmentation of tumor images. This article summarizes the latest studies of deep learning techniques applied to three different kinds of brain cancer medical images (histology, magnetic resonance, and computed tomography) and highlights current challenges in the field for the broader applicability of DCNN in personalized brain cancer care by focusing on two main applications of DCNNs: classification and segmentation of brain cancer tumors images.

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Brain Tumor Segmentation Using OTSU Embedded Adaptive Particle Swarm Optimization Method and Convolutional Neural Network

Cited by 50 publications

References 58 publications

Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network

Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network

Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists

The Application of Deep Convolutional Neural Networks to Brain Cancer Images: A Survey

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