Deep CNN for Brain Tumor Classification

Ayadi, Wadhah; Elhamzi, Wajdi; Charfi, Imen; Atri, Mohamed

doi:10.1007/s11063-020-10398-2

Cited by 200 publications

(100 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…So, it requires automatic detection using computer assisted diagnosis (CAD). In this research [18] , Wadhah Ayadi et al deep convolution neural network (CNN) is developed to classify the tumor in an accurate manner. This deep CNN model is evaluated against three different kinds of datasets.…”

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.

View full text Add to dashboard Cite

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.

show abstract

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.

View full text Add to dashboard Cite

show abstract

“…Lately, deep learning delivered important success in several fields such as pattern recognition ( [34], [35]), medical imaging [36], and biometrics [37]. Shape information extracted from deep networks is very limited [10].…”

Section: Deep Cnn Classifiermentioning

confidence: 99%

Combination of Texture and Shape Features Using Machine and Deep Learning Algorithms for Breast Cancer Diagnosis

Gargouri

Mokni

Damak³

et al. 2021

Preprint

View full text Add to dashboard Cite

Worldwide, breast cancer is a commonly occurring disease in women. Automatic diagnosis of the lesions based on mammographic images is playing an essential role to assist experts. A novel Computer-Aided Diagnosis (CADx) scheme of breast lesion classification is proposed in this paper based on an optimized combination of texture and shape features using machine and deep learning algorithms for mass classification as benign-malignant namely C(M-ZMs)*. The main advantage of using Zernike moments for shape feature extraction is their scale, translation, and rotation invariance property, this allows omitting some of the preprocessing stages in our case. We implemented for texture feature extraction the Monogenic-Local Binary Pattern taking the advantage of lower time and space complexity because monogenic signal analysis needs fewer convolutions and generates more compact feature vectors. Therefore, we used Zernike moments for shape feature extraction due to their scale, translation, and rotation invariance property, this allows omitting some of the preprocessing stages in our proposed system. The proposed system proves its performance on some challenging breast cancer cases where the lesions exist in dense breast tissues. Validation has been undertaken on 520 mammograms from the Digital Database for Screening Mammography Database (DDSM), yielding an accuracy rate of 99.5\%.

show abstract

“…These methods detect tumors on only Flair imaging modality such that SVM has been utilized for classification that performed better on small data. Hence, there is still a need of improved techniques for tumors detection on different views, such as saggital, coronal, and axial from large-scale imaging data [5,14]. Keeping this in view, an improved approach is presented in this article for classification, localization, and segmentation of glioma lesions.…”

Section: Introductionmentioning

confidence: 99%

An improved framework for brain tumor analysis using MRI based on YOLOv2 and convolutional neural network

Sharif

Amin

et al. 2021

Complex Intell. Syst.

View full text Add to dashboard Cite

Brain tumor is a group of anomalous cells. The brain is enclosed in a more rigid skull. The abnormal cell grows and initiates a tumor. Detection of tumor is a complicated task due to irregular tumor shape. The proposed technique contains four phases, which are lesion enhancement, feature extraction and selection for classification, localization, and segmentation. The magnetic resonance imaging (MRI) images are noisy due to certain factors, such as image acquisition, and fluctuation in magnetic field coil. Therefore, a homomorphic wavelet filer is used for noise reduction. Later, extracted features from inceptionv3 pre-trained model and informative features are selected using a non-dominated sorted genetic algorithm (NSGA). The optimized features are forwarded for classification after which tumor slices are passed to YOLOv2-inceptionv3 model designed for the localization of tumor region such that features are extracted from depth-concatenation (mixed-4) layer of inceptionv3 model and supplied to YOLOv2. The localized images are passed to McCulloch's Kapur entropy method to segment actual tumor region. Finally, the proposed technique is validated on three benchmark databases BRATS 2018, BRATS 2019, and BRATS 2020 for tumor detection. The proposed method achieved greater than 0.90 prediction scores in localization, segmentation and classification of brain lesions. Moreover, classification and segmentation outcomes are superior as compared to existing methods.

show abstract

Deep CNN for Brain Tumor Classification

Cited by 200 publications

References 64 publications

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

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

Combination of Texture and Shape Features Using Machine and Deep Learning Algorithms for Breast Cancer Diagnosis

An improved framework for brain tumor analysis using MRI based on YOLOv2 and convolutional neural network

Contact Info

Product

Resources

About