A brain tumor is a mass of abnormal cells in the brain. Brain tumors can be benign (noncancerous) or malignant (cancerous). Conventional diagnosis of a brain tumor by the radiologist is done by examining a set of imag...A brain tumor is a mass of abnormal cells in the brain. Brain tumors can be benign (noncancerous) or malignant (cancerous). Conventional diagnosis of a brain tumor by the radiologist is done by examining a set of images produced by magnetic resonance imaging (MRI). Many computer-aided detection (CAD) systems have been developed in order to help the radiologists reach their goal of correctly classifying the MRI image. Convolutional neural networks (CNNs) have been widely used in the classification of medical images. This paper presents a novel CAD technique for the classification of brain tumors in MRI images. The proposed system extracts features from the brain MRI images by utilizing the strong energy compactness property exhibited by the Discrete Wavelet Transform (DWT). The Wavelet features are then applied to a CNN to classify the input MRI image. Experimental results indicate that the proposed approach outperforms other commonly used methods and gives an overall accuracy of 99.3%.展开更多
Objective Artificial neural network is first used in the measurement study of brain of Alzheimer's disease using MRI, and a completely new pattern discriminating method is adopted, so as to take advantage of MRI ...Objective Artificial neural network is first used in the measurement study of brain of Alzheimer's disease using MRI, and a completely new pattern discriminating method is adopted, so as to take advantage of MRI to diagnose and identify AD patients. Methods 12 patients with probable AD (aged 65.33±8.62 years) and 36 normal controls matched with age and gender (aged 65.81±7.37 years) were studied. MRI are performed on Siemens Magnetom IMPACT 1.0 T; eight interesting brain structures including sixteen regions (left and right) indices are measured and studied; SPSS software and BP network software made by authors respectively were used to process and analyze the measured data. Results Using artificial neural network to the same regions and data, both the sensitivity and accuracy were found higher than using the traditional discrimination function analysis method; the indices of amygdala, hippocampus, parahippocampal gyrus, temporal lobe, and temporal horn, these five structures could completely differentiate AD from normal controls; new cases were successfully diagnosed. Conclusions Artificial neural network combining with MRI is probable to become a useful and reliable clinical tool to diagnose AD patients.展开更多
Abnormal growth of brain tissues is the real cause of brain tumor.Strategy for the diagnosis of brain tumor at initial stages is one of the key step for saving the life of a patient.The manual segmentation of brain tu...Abnormal growth of brain tissues is the real cause of brain tumor.Strategy for the diagnosis of brain tumor at initial stages is one of the key step for saving the life of a patient.The manual segmentation of brain tumor magnetic resonance images(MRIs)takes time and results vary significantly in low-level features.To address this issue,we have proposed a ResNet-50 feature extractor depended on multilevel deep convolutional neural network(CNN)for reliable images segmentation by considering the low-level features of MRI.In this model,we have extracted features through ResNet-50 architecture and fed these feature maps to multi-level CNN model.To handle the classification process,we have collected a total number of 2043 MRI patients of normal,benign,and malignant tumor.Three model CNN,multi-level CNN,and ResNet-50 based multi-level CNN have been used for detection and classification of brain tumors.All the model results are calculated in terms of various numerical values identified as precision(P),recall(R),accuracy(Acc)and f1-score(F1-S).The obtained average results are much better as compared to already existing methods.This modified transfer learning architecture might help the radiologists and doctors as a better significant system for tumor diagnosis.展开更多
文摘A brain tumor is a mass of abnormal cells in the brain. Brain tumors can be benign (noncancerous) or malignant (cancerous). Conventional diagnosis of a brain tumor by the radiologist is done by examining a set of images produced by magnetic resonance imaging (MRI). Many computer-aided detection (CAD) systems have been developed in order to help the radiologists reach their goal of correctly classifying the MRI image. Convolutional neural networks (CNNs) have been widely used in the classification of medical images. This paper presents a novel CAD technique for the classification of brain tumors in MRI images. The proposed system extracts features from the brain MRI images by utilizing the strong energy compactness property exhibited by the Discrete Wavelet Transform (DWT). The Wavelet features are then applied to a CNN to classify the input MRI image. Experimental results indicate that the proposed approach outperforms other commonly used methods and gives an overall accuracy of 99.3%.
文摘Objective Artificial neural network is first used in the measurement study of brain of Alzheimer's disease using MRI, and a completely new pattern discriminating method is adopted, so as to take advantage of MRI to diagnose and identify AD patients. Methods 12 patients with probable AD (aged 65.33±8.62 years) and 36 normal controls matched with age and gender (aged 65.81±7.37 years) were studied. MRI are performed on Siemens Magnetom IMPACT 1.0 T; eight interesting brain structures including sixteen regions (left and right) indices are measured and studied; SPSS software and BP network software made by authors respectively were used to process and analyze the measured data. Results Using artificial neural network to the same regions and data, both the sensitivity and accuracy were found higher than using the traditional discrimination function analysis method; the indices of amygdala, hippocampus, parahippocampal gyrus, temporal lobe, and temporal horn, these five structures could completely differentiate AD from normal controls; new cases were successfully diagnosed. Conclusions Artificial neural network combining with MRI is probable to become a useful and reliable clinical tool to diagnose AD patients.
基金Authors would like to acknowledge the support of the Deputy for Research and Innovation-Ministry of Education,Kingdom of Saudi Arabia for funding this research through a project(NU/IFC/ENT/01/014)under the institutional funding committee at Najran University,Kingdom of Saudi Arabia.
文摘Abnormal growth of brain tissues is the real cause of brain tumor.Strategy for the diagnosis of brain tumor at initial stages is one of the key step for saving the life of a patient.The manual segmentation of brain tumor magnetic resonance images(MRIs)takes time and results vary significantly in low-level features.To address this issue,we have proposed a ResNet-50 feature extractor depended on multilevel deep convolutional neural network(CNN)for reliable images segmentation by considering the low-level features of MRI.In this model,we have extracted features through ResNet-50 architecture and fed these feature maps to multi-level CNN model.To handle the classification process,we have collected a total number of 2043 MRI patients of normal,benign,and malignant tumor.Three model CNN,multi-level CNN,and ResNet-50 based multi-level CNN have been used for detection and classification of brain tumors.All the model results are calculated in terms of various numerical values identified as precision(P),recall(R),accuracy(Acc)and f1-score(F1-S).The obtained average results are much better as compared to already existing methods.This modified transfer learning architecture might help the radiologists and doctors as a better significant system for tumor diagnosis.
