scholarly journals A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI images

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 744
Author(s):  
Momina Masood ◽  
Tahira Nazir ◽  
Marriam Nawaz ◽  
Awais Mehmood ◽  
Junaid Rashid ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Abnormal Growth ◽  
Irregular Shapes ◽  
Proposed Model ◽  
Benchmark Datasets ◽  
Domain Expertise ◽  
Comparative Results ◽  
Bounding Boxes

A brain tumor is an abnormal growth in brain cells that causes damage to various blood vessels and nerves in the human body. An earlier and accurate diagnosis of the brain tumor is of foremost important to avoid future complications. Precise segmentation of brain tumors provides a basis for surgical planning and treatment to doctors. Manual detection using MRI images is computationally complex in cases where the survival of the patient is dependent on timely treatment, and the performance relies on domain expertise. Therefore, computerized detection of tumors is still a challenging task due to significant variations in their location and structure, i.e., irregular shapes and ambiguous boundaries. In this study, we propose a custom Mask Region-based Convolution neural network (Mask RCNN) with a densenet-41 backbone architecture that is trained via transfer learning for precise classification and segmentation of brain tumors. Our method is evaluated on two different benchmark datasets using various quantitative measures. Comparative results show that the custom Mask-RCNN can more precisely detect tumor locations using bounding boxes and return segmentation masks to provide exact tumor regions. Our proposed model achieved an accuracy of 96.3% and 98.34% for segmentation and classification respectively, demonstrating enhanced robustness compared to state-of-the-art approaches.

scholarly journals Brain Tumor Detection with Deep Learning on MRI Image Dataset

2021 ◽  
Vol 9 (VI) ◽  
pp. 2144-2148
Author(s):  
S. Rakesh
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Good Accuracy ◽  
Deep Convolutional Neural Networks ◽  
Abnormal Growth ◽  
Proposed Model ◽  
Image Dataset ◽  
Scanned Images ◽  
The Brain ◽  
Mri Image

Tumor is an unusual growth of the tissues in any part or organ of the human body. A brain tumor is an abnormal growth of cells in the brain or central spine canal. Brain tumors can be malignant (cancerous) or benign (non-cancerous) and have different treatments. Any extra growth of cells in the brain can result in pressure inside the skull as it is rigid and hence damages the brain. In the recent years, a progressive research has been done in this medical imaging field to more accurately detect the brain tumors. This is very important for early treatment. The proposed model detects the location and dimensions of the tumor present in the brain of a patient accurately using deep convolutional neural networks. In this paper we have presented the two models, Inception V3 and VGG-Net. The main aim of this paper is to find the best technique to detect the brain tumor from the MRI scanned images. The proposed models have given good results with good accuracy.

scholarly journals Differential Deep Convolutional Neural Network Model for Brain Tumor Classification

2021 ◽  
Vol 11 (3) ◽  
pp. 352
Author(s):  
Isselmou Abd El Kader ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Sani Saminu ◽  
Imran Javaid ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Medical Image ◽  
Resonance Imaging ◽  
Feature Maps ◽  
Deep Cnn

The classification of brain tumors is a difficult task in the field of medical image analysis. Improving algorithms and machine learning technology helps radiologists to easily diagnose the tumor without surgical intervention. In recent years, deep learning techniques have made excellent progress in the field of medical image processing and analysis. However, there are many difficulties in classifying brain tumors using magnetic resonance imaging; first, the difficulty of brain structure and the intertwining of tissues in it; and secondly, the difficulty of classifying brain tumors due to the high density nature of the brain. We propose a differential deep convolutional neural network model (differential deep-CNN) to classify different types of brain tumor, including abnormal and normal magnetic resonance (MR) images. Using differential operators in the differential deep-CNN architecture, we derived the additional differential feature maps in the original CNN feature maps. The derivation process led to an improvement in the performance of the proposed approach in accordance with the results of the evaluation parameters used. The advantage of the differential deep-CNN model is an analysis of a pixel directional pattern of images using contrast calculations and its high ability to classify a large database of images with high accuracy and without technical problems. Therefore, the proposed approach gives an excellent overall performance. To test and train the performance of this model, we used a dataset consisting of 25,000 brain magnetic resonance imaging (MRI) images, which includes abnormal and normal images. The experimental results showed that the proposed model achieved an accuracy of 99.25%. This study demonstrates that the proposed differential deep-CNN model can be used to facilitate the automatic classification of brain tumors.

scholarly journals Classification & Feature extraction of Brain tumor from MRI Images using Modified ANN Approach

2021 ◽  
Vol 9 (2) ◽  
pp. 10-15
Author(s):  
Harendra Singh ◽  
Roop Singh Solanki
Keyword(s):  
Feature Extraction ◽  
Brain Tumor ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Classification Feature ◽  
Proposed Model ◽  
Magnetic Resonance Imaging Mri ◽  
Artificial Neural Network Ann ◽  
High Degree

In this research paper, a new modified approach is proposed for brain tumor classification as well as feature extraction from Magnetic Resonance Imaging (MRI) after pre-processing of the images. The discrete wavelet transformation (DWT) technique is used for feature extraction from MRI images and Artificial Neural Network (ANN) is used for the classification of the type of tumor according to extracted features. Mean, Standard deviation, Variance, Entropy, Skewness, Homogeneity, Contrast, Correlation are the main features used to classify the type of tumor. The proposed model can give a better result in comparison with other available techniques in less computational time as well as a high degree of accuracy. The training and testing accuracies of the proposed model are 100% and 98.20% with a 98.70 % degree of precision respectively.

scholarly journals Classification of Tumors in Brain MRI Images With Hybrid of Global and Local DWT Features using Decision Tree

2019 ◽  
Vol 8 (12) ◽  
pp. 3072-3077
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Decision Tree ◽  
Brain Mri ◽  
Extraction Process ◽  
Discrete Wavelet ◽  
Global Features ◽  
Tumor Identification ◽  
The Brain

Automated brain tumor identification and classification is still an open problem for research in the medical image processing domain. Brain tumor is a bunch of unwanted cells that develop in the brain. This growth of a tumor takes up space within skull and affects the normal functioning of brain. Automated segmentation and detection of brain tumors are important in MRI scan analysis as it provides information about neural architecture of brain and also about abnormal tissues that are extremely necessary to identify appropriate surgical plan. Automating this process is a challenging task as tumor tissues show high diversity in appearance with different patients and also in many cases they tend to appear very similar to the normal tissues. Effective extraction of features that represent the tumor in brain image is the key for better classification. In this paper, we propose a hybrid feature extraction process. In this process, we combine the local and global features of the brain MRI using first by Discrete Wavelet Transformation and then using texture based statistical features by computing Gray Level Co-occurrence Matrix. The extracted combined features are used to construct decision tree for classification of brain tumors in to benign or malignant class.

scholarly journals Convolutional Neural Network Integrated With Fuzzy Rules for Decision Making in Brain Tumor Diagnosis

2021 ◽  
Vol 15 (4) ◽  
pp. 1-23
Author(s):  
Pham Van Hai ◽  
Samson Eloanyi Amaechi
Keyword(s):  
Neural Networks ◽  
Medical Imaging ◽  
Brain Tumors ◽  
Convolutional Neural Networks ◽  
Fuzzy Rules ◽  
Experimental Result ◽  
Support Vector ◽  
Proposed Model ◽  
Diagnosis And Classification

Conventional methods used in brain tumors detection, diagnosis, and classification such as magnetic resonance imaging and computed tomography scanning technologies are unbridged in their results. This paper presents a proposed model combination, convolutional neural networks with fuzzy rules in the detection and classification of medical imaging such as healthy brain cell and tumors brain cells. This model contributes fully on the automatic classification and detection medical imaging such as brain tumors, heart diseases, breast cancers, HIV and FLU. The experimental result of the proposed model shows overall accuracy of 97.6%, which indicates that the proposed method achieves improved performance than the other current methods in the literature such as [classification of tumors in human brain MRI using wavelet and support vector machine 94.7%, and deep convolutional neural networks with transfer learning for automated brain image classification 95.0%], uses in the detection, diagnosis, and classification of medical imaging decision supports.

scholarly journals Analysis of Brain MRI Images Using Improved CornerNet Approach

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1856
Author(s):  
Marriam Nawaz ◽  
Tahira Nazir ◽  
Momina Masood ◽  
Awais Mehmood ◽  
Rabbia Mahum ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Brain Mri ◽  
Region Of Interest ◽  
Abnormal Growth ◽  
Novel Approach ◽  
Human Blood Cells ◽  
Average Accuracy ◽  
The One ◽  
Treatment Procedures

The brain tumor is a deadly disease that is caused by the abnormal growth of brain cells, which affects the human blood cells and nerves. Timely and precise detection of brain tumors is an important task to avoid complex and painful treatment procedures, as it can assist doctors in surgical planning. Manual brain tumor detection is a time-consuming activity and highly dependent on the availability of area experts. Therefore, it is a need of the hour to design accurate automated systems for the detection and classification of various types of brain tumors. However, the exact localization and categorization of brain tumors is a challenging job due to extensive variations in their size, position, and structure. To deal with the challenges, we have presented a novel approach, namely, DenseNet-41-based CornerNet framework. The proposed solution comprises three steps. Initially, we develop annotations to locate the exact region of interest. In the second step, a custom CornerNet with DenseNet-41 as a base network is introduced to extract the deep features from the suspected samples. In the last step, the one-stage detector CornerNet is employed to locate and classify several brain tumors. To evaluate the proposed method, we have utilized two databases, namely, the Figshare and Brain MRI datasets, and attained an average accuracy of 98.8% and 98.5%, respectively. Both qualitative and quantitative analysis show that our approach is more proficient and consistent with detecting and classifying various types of brain tumors than other latest techniques.

scholarly journals Development of Deep Learning Algorithm for Brain Tumor Segmentation

2019 ◽  
Vol 9 (1) ◽  
pp. 2800-2803
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Learning Algorithm ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Human Errors ◽  
Deep Learning Algorithm ◽  
Proposed Model ◽  
A New Technique

Medical imaging is an emerging field in engineering. As traditional way of brain tumor analysis, MRI scanning is the way to identify brain tumor. The core drawback of manual MRI studies conducted by surgeons is getting manual visual errorswhich can lead toofa false identification of tumor boundaries. To avoid such human errors, ultra age engineering adopted deep learning as a new technique for brain tumor segmentation. Deep learning convolution network can be further developed by means of various deep learning models for better performance. Hence, we proposed a new deep learning algorithm development which can more efficiently identifies the types of brain tumors in terms of level of tumor like T1, T2, and T1ce etc. The proposed system can identify tumors using convolution neural network(CNN) which works with the proposed algorithm “Sculptor DeepCNet”. The proposed model can be used by surgeons to identify post-surgical remains (if any) of brain tumors and thus proposed research can be useful for ultra-age neural surgical image assessments. This paper discusses newly developed algorithm and its testing results.

scholarly journals A Review on Some Classification Methods of Brain Tumor MRI Datasets

2020 ◽  
pp. 421-424
Author(s):  
Nirmal Mungale ◽  
Snehal Kene ◽  
Amol Chaudhary
Keyword(s):  
Brain Tumor ◽  
Brain Mri ◽  
Mr Images ◽  
Classification Techniques ◽  
Abnormal Growth ◽  
Life Threatening ◽  
Magnetic Resonance Imaging Mri ◽  
Recent Classification ◽  
The Brain

Brain tumor is a life-threatening disease. Brain tumor is formed by the abnormal growth of cells inside and around the brain. Identification of the size and type of tumor is necessary for deciding the course of treatment of the patient. Magnetic Resonance Imaging (MRI) is one of the methods for detection of tumor in the brain. The classification of MR Images is a difficult task due to variety and complexity of brain tumors. Various classification techniques have been identified for brain MRI tumor images. This paper reviews some of these recent classification techniques.

Predicting enhancer-promoter interactions by deep learning and matching heuristic

2020 ◽  
Author(s):  
Xiaoping Min ◽  
Congmin Ye ◽  
Xiangrong Liu ◽  
Xiangxiang Zeng
Keyword(s):  
Dna Sequences ◽  
Predictive Performance ◽  
Proposed Model ◽  
Computational Speed ◽  
Benchmark Datasets ◽  
Multiple Cell ◽  
Novel Method ◽  
Comparative Results ◽  
Genome Scale ◽  
Gated Recurrent Unit

Abstract Enhancer-promoter interactions (EPIs) play an important role in transcriptional regulation. Recently, machine learning-based methods have been widely used in the genome-scale identification of EPIs due to their promising predictive performance. In this paper, we propose a novel method, termed EPI-DLMH, for predicting EPIs with the use of DNA sequences only. EPI-DLMH consists of three major steps. First, a two-layer convolutional neural network is used to learn local features, and an bidirectional gated recurrent unit network is used to capture long-range dependencies on the sequences of promoters and enhancers. Second, an attention mechanism is used for focusing on relatively important features. Finally, a matching heuristic mechanism is introduced for the exploration of the interaction between enhancers and promoters. We use benchmark datasets in evaluating and comparing the proposed method with existing methods. Comparative results show that our model is superior to currently existing models in multiple cell lines. Specifically, we found that the matching heuristic mechanism introduced into the proposed model mainly contributes to the improvement of performance in terms of overall accuracy. Additionally, compared with existing models, our model is more efficient with regard to computational speed.

scholarly journals Histopathological Classification of Breast Cancer Images Using a Multi-Scale Input and Multi-Feature Network

Cancers ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2031 ◽  
Author(s):  
Taimoor Shakeel Sheikh ◽  
Yonghee Lee ◽  
Migyung Cho
Keyword(s):  
State Of The Art ◽  
Texture Features ◽  
Feature Maps ◽  
Histopathological Classification ◽  
Multi Scale ◽  
Machine Learning Methods ◽  
Proposed Model ◽  
Benchmark Datasets ◽  
Histopathological Images

Diagnosis of pathologies using histopathological images can be time-consuming when many images with different magnification levels need to be analyzed. State-of-the-art computer vision and machine learning methods can help automate the diagnostic pathology workflow and thus reduce the analysis time. Automated systems can also be more efficient and accurate, and can increase the objectivity of diagnosis by reducing operator variability. We propose a multi-scale input and multi-feature network (MSI-MFNet) model, which can learn the overall structures and texture features of different scale tissues by fusing multi-resolution hierarchical feature maps from the network’s dense connectivity structure. The MSI-MFNet predicts the probability of a disease on the patch and image levels. We evaluated the performance of our proposed model on two public benchmark datasets. Furthermore, through ablation studies of the model, we found that multi-scale input and multi-feature maps play an important role in improving the performance of the model. Our proposed model outperformed the existing state-of-the-art models by demonstrating better accuracy, sensitivity, and specificity.

Sign in / Sign up

Export Citation Format

Share Document