Computer-Aided Diagnosis of Malign and Benign Brain Tumors on MR Images

2015 ◽  
pp. 157-166 ◽  
Author(s):  
Emre Dandıl ◽  
Murat Çakıroğlu ◽  
Ziya Ekşi
Keyword(s):  
Brain Tumors ◽  
Computer Aided Diagnosis ◽  
Mr Images ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Computer-Aided Diagnosis for Detection of Lacunar Infarcts on MR Images: ROC Analysis of Radiologists’ Performance

2012 ◽  
Vol 25 (4) ◽  
pp. 497-503 ◽  
Author(s):  
Yoshikazu Uchiyama ◽  
Takahiko Asano ◽  
Hiroki Kato ◽  
Takeshi Hara ◽  
Masayuki Kanematsu ◽  
...  
Keyword(s):  
Roc Analysis ◽  
Computer Aided Diagnosis ◽  
Mr Images ◽  
Lacunar Infarcts ◽  
Computer Aided ◽  
Aided Diagnosis

Computer-Aided Diagnosis Scheme for Detection of Lacunar Infarcts on MR Images

2007 ◽  
Vol 14 (12) ◽  
pp. 1554-1561 ◽  
Author(s):  
Yoshikazu Uchiyama ◽  
Ryujiro Yokoyama ◽  
Hiromich Ando ◽  
Takahiko Asano ◽  
Hiroki Kato ◽  
...  
Keyword(s):  
Computer Aided Diagnosis ◽  
Mr Images ◽  
Lacunar Infarcts ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals COMPUTER-AIDED DIAGNOSIS OF BRAIN TUMORS USING IMAGE ENHANCEMENT AND FUZZY LOGIC

DYNA ◽  
2014 ◽  
Vol 81 (183) ◽  
pp. 148 ◽  
Author(s):  
JEAN MARIE VIANNEY-KINANI ◽  
ALBERTO J. ROSALES-SILVA ◽  
FRANCISCO J. GALLEGOS-FUNES ◽  
ALFONSO ARELLANO
Keyword(s):  
Fuzzy Logic ◽  
Brain Tumors ◽  
Image Enhancement ◽  
Computer Aided Diagnosis ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals PBTNet: A New Computer-Aided Diagnosis System for Detecting Primary Brain Tumors

2021 ◽  
Vol 9 ◽  
Author(s):  
Si-Yuan Lu ◽  
Suresh Chandra Satapathy ◽  
Shui-Hua Wang ◽  
Yu-Dong Zhang
Keyword(s):  
Magnetic Resonance ◽  
Brain Tumors ◽  
Computer Aided Diagnosis ◽  
Magnetic Resonance Images ◽  
Brain Diseases ◽  
Functional Link ◽  
Diagnosis System ◽  
Primary Brain Tumors ◽  
Computer Aided ◽  
Aided Diagnosis

Brain tumors are among the leading human killers. There are over 120 different types of brain tumors, but they mainly fall into two groups: primary brain tumors and metastatic brain tumors. Primary brain tumors develop from normal brain cells. Early and accurate detection of primary brain tumors is vital for the treatment of this disease. Magnetic resonance imaging is the most common method to diagnose brain diseases, but the manual interpretation of the images suffers from high inter-observer variance. In this paper, we presented a new computer-aided diagnosis system named PBTNet for detecting primary brain tumors in magnetic resonance images. A pre-trained ResNet-18 was selected as the backbone model in our PBTNet, but it was fine-tuned only for feature extraction. Then, three randomized neural networks, Schmidt neural network, random vector functional-link, and extreme learning machine served as the classifiers in the PBTNet, which were trained with the features and their labels. The final predictions of the PBTNet were generated by the ensemble of the outputs from the three classifiers. 5-fold cross-validation was employed to evaluate the classification performance of the PBTNet, and experimental results demonstrated that the proposed PBTNet was an effective tool for the diagnosis of primary brain tumors.

Application of AI for Computer-Aided Diagnosis System to Detect Brain Tumors

2021 ◽  
pp. 185-204
Author(s):  
Poulomi Das ◽  
Rahul Rajak ◽  
Arpita Das
Keyword(s):  
Brain Tumors ◽  
Contrast Enhancement ◽  
Performance Metrics ◽  
Brain Mri ◽  
Computer Aided Diagnosis ◽  
Patient Death ◽  
Medical Practitioners ◽  
Cad System ◽  
Computer Aided ◽  
Aided Diagnosis

Early detection and proper treatment of brain tumors are imperative to prevent permanent damage to the brain even patient death. The present study proposed an AI-based computer-aided diagnosis (CAD) system that refers to the process of automated contrast enhancement followed by identifying the region of interest (ROI) and then classify ROI into benign/malignant classes using significant morphological feature selection. This tool automates the detection procedure and also reduces the manual efforts required in widespread screening of brain MRI. Simple power law transformation technique based on different performance metrics is used to automate the contrast enhancement procedure. Finally, benignancy/malignancy of brain tumor is examined by neural network classifier and its performance is assessed by well-known receiver operating characteristic method. The result of the proposed method is enterprising with very low computational time and accuracy of 87.8%. Hence, the proposed method of CAD procedure may encourage the medical practitioners to get alternative opinion.

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