A simplified framework for the detection of intracranial hemorrhage in CT brain images using deep learning

2021 ◽  
Vol 17 ◽  
Author(s):  
Praveen K ◽  
Sasikala M ◽  
Janani A ◽  
Nijisha Shajil ◽  
Hari Nishanthi V
Keyword(s):  
Deep Learning ◽  
Intracranial Hemorrhage ◽  
Feature Selection Method ◽  
Ct Images ◽  
Support Vector ◽  
Svm Classifier ◽  
Brain Images ◽  
Ct Brain ◽  
Ct Brain Images

Background: The need for accurate and timely detection of Intracranial hemorrhage (ICH) is utmost important to avoid untoward incidents that may even lead to death.Hence, this presented work leverages the ability of a pretrained deep convolutional neural network (CNN) for the detection of ICH in computed tomography (CT) brain images. Methods: Different frameworks have been analyzed for their effectiveness for the classification of CT brain images into hemorrhage or non-hemorrhage conditions. All these frameworks were investigated on CQ500 dataset. Furthermore, an exclusive preprocessing pipeline was designed for both normal and ICH CT images. Firstly, a framework involving the pretrained deep CNN, AlexNet, has been exploited for both feature extraction and classification using the transfer learning method, secondly, a modified AlexNet-Support vector machine (SVM) classifier is explored and finally, a feature selection method, Principal Component Analysis (PCA) has been introduced in the AlexNet-SVM classifier model and its efficacy is explored.These models were trained and tested on two different sets of CT images, one containing the original images without preprocessing and another set consisting of preprocessed images. Results: The modified AlexNet-SVM classifier has shown an improved performance in comparison to the other investigated frameworks and has achieved a classification accuracy of 99.86%, sensitivity and specificity of 0.9986 for the detection of ICH in brain CT images. Conclusion: This research has given an overview of a simple and efficient framework for the classification of hemorrhage and non-hemorrhage images. Also, the proposed simplified deep learning framework manifests its ability as a screening tool to assist the radiological trainees for the accurate detection of ICH.

scholarly journals Security Challenges and Application for Underwater Wireless Sensor Network

10.29007/ctsn ◽  
2018 ◽  
Author(s):  
Sarvesh Kumar Kumar ◽  
Bersha Kumari ◽  
Harshita Chawla
Keyword(s):  
Ct Images ◽  
Kernel Functions ◽  
Support Vector ◽  
Svm Classifier ◽  
Second Phase ◽  
Normal Brain ◽  
Brain Images ◽  
Brain Ct ◽  
Automated Method ◽  
Occurrence Matrix

Automated detection of the abnormalities in brain image analysis is very important and it is prerequisite for planning and treatment of the disease. Computed tomography scan is an imaging technique used for studying brain images. Classification of brain images is important in order to distinguish between normal brain images and those having the abnormalities in brain like hematomas, tumor, edema, concussion etc. The proposed automated method identifies the abnormalities in brain CT images and classifies them using support vector machine. The proposed method consists of three important phases, First phase is preprocessing, second phase consists of feature extraction and final phase is classification. In the first phase preprocessing is performed on brain CT images to remove artifacts and noise. In second phase features are extracted from brain CT images using gray level co-occurrence matrix (GLCM). In the final stage, extracted features are fed as input to SVM classifier with different kernel functions that classifies the images into normal and abnormal with different accuracy levels.

DELINEATION AND CLASSIFICATION OF LIVER CANCER USING LEVEL SET METHOD IN CT IMAGES

2017 ◽  
Vol 29 (06) ◽  
pp. 1750047
Author(s):  
Amita Das ◽  
S. S. Panda ◽  
Sukanta Sabut
Keyword(s):  
Machine Learning ◽  
Level Set ◽  
Texture Classification ◽  
Ct Images ◽  
Experimental Result ◽  
Support Vector ◽  
Svm Classifier ◽  
Correct Prediction ◽  
Liver Cancers

The paper proposes a modified approach of delineation and classification of two different types of liver cancers viz. Hepatocellular Carcinoma (HCC) and Metastatic Carcinoma (MET) from different slices of computed tomography (CT) scans images. A combined framework of reorganization and extraction of region of interest (ROI), texture feature extraction followed by texture classification by different machine learning approaches has been presented. Initially, adaptive thresholding has been applied to segment the liver region from CT images. Level set algorithm has been used for detecting the region of cancer tissues. In the classification stage, the delineated output lesions have been extracted with 38 features to build up the dataset. Two machine learning classifiers, support vector machine (SVM) and random forest (RF), have been used to train the dataset for correct prediction of cancer classes. Ten-fold cross-validation has been used to evaluate the performance of two classifiers. The efficiency of the proposed algorithm is tested in terms of accuracy, where the RF classifier achieved a higher accuracy of 95% compared to SVM classifier of 87%. The experimental result proves the superiority of RF classifier compared to SVM classifier with level-set features.

scholarly journals Abnormality Detection in Brain CT Image Using Support Vector Machine

10.29007/jsfg ◽  
2018 ◽  
Author(s):  
Bhavna Sharma ◽  
Priyanka Mitra
Keyword(s):  
Support Vector Machine ◽  
Ct Images ◽  
Kernel Functions ◽  
Support Vector ◽  
Svm Classifier ◽  
Second Phase ◽  
Normal Brain ◽  
Brain Images ◽  
Brain Ct ◽  
Automated Method

Automated detection of the abnormalities in brain image analysis is very important and it is prerequisite for planning and treatment of the disease. Computed tomography scan is an imaging technique used for studying brain images. Classification of brain images is important in order to distinguish between normal brain images and those having the abnormalities in brain like hematomas, tumor, edema, concussion etc. The proposed automated method identifies the abnormalities in brain CT images and classifies them using support vector machine. The proposed method consists of three important phases, First phase is preprocessing, second phase consists of feature extraction and final phase is classification. In the first phase preprocessing is performed on brain CT images to remove artifacts and noise. In second phase features are extracted from brain CT images using gray level co-occurrence matrix (GLCM). In the final stage, extracted features are fed as input to SVM classifier with different kernel functions that classifies the images into normal and abnormal with different accuracy levels.

scholarly journals Classification of CT Brain Images of Head Trauma

2007 ◽  
pp. 401-408 ◽  
Author(s):  
Tianxia Gong ◽  
Ruizhe Liu ◽  
Chew Lim Tan ◽  
Neda Farzad ◽  
Cheng Kiang Lee ◽  
...  
Keyword(s):  
Head Trauma ◽  
Brain Images ◽  
Ct Brain ◽  
Ct Brain Images

A fuzzy gaussian rank aggregation ensemble feature selection method for microarray data

2021 ◽  
Vol 24 (4) ◽  
pp. 289-301
Author(s):  
B. Venkatesh ◽  
J. Anuradha
Keyword(s):  
Feature Selection ◽  
Microarray Data ◽  
Classification Accuracy ◽  
Performance Metrics ◽  
Feature Selection Method ◽  
Selection Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Binary Particle Swarm Optimization ◽  
Selection Methods

In Microarray Data, it is complicated to achieve more classification accuracy due to the presence of high dimensions, irrelevant and noisy data. And also It had more gene expression data and fewer samples. To increase the classification accuracy and the processing speed of the model, an optimal number of features need to extract, this can be achieved by applying the feature selection method. In this paper, we propose a hybrid ensemble feature selection method. The proposed method has two phases, filter and wrapper phase in filter phase ensemble technique is used for aggregating the feature ranks of the Relief, minimum redundancy Maximum Relevance (mRMR), and Feature Correlation (FC) filter feature selection methods. This paper uses the Fuzzy Gaussian membership function ordering for aggregating the ranks. In wrapper phase, Improved Binary Particle Swarm Optimization (IBPSO) is used for selecting the optimal features, and the RBF Kernel-based Support Vector Machine (SVM) classifier is used as an evaluator. The performance of the proposed model are compared with state of art feature selection methods using five benchmark datasets. For evaluation various performance metrics such as Accuracy, Recall, Precision, and F1-Score are used. Furthermore, the experimental results show that the performance of the proposed method outperforms the other feature selection methods.

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