Automated Brain Tumor Diagnosis and Severity Analysis from Brain MRI

2017 ◽  
pp. 194-207
Author(s):  
Sabyasachi Mukherjee ◽  
Oishila Bandyopadhyay ◽  
Arindam Biswas
Keyword(s):  
Brain Tumor ◽  
Brain Mri ◽  
Tumor Diagnosis ◽  
Brain Tumor Diagnosis

scholarly journals Computer-Aided Brain Tumor Diagnosis: Performance Evaluation of Deep Learner CNN Using Augmented Brain MRI

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Asma Naseer ◽  
Tahreem Yasir ◽  
Arifah Azhar ◽  
Tanzeela Shakeel ◽  
Kashif Zafar
Keyword(s):  
Brain Tumor ◽  
Early Diagnosis ◽  
Brain Mri ◽  
Magnetic Resonance Images ◽  
Tumor Diagnosis ◽  
Survival Ratio ◽  
Unseen Data ◽  
Computer Aided ◽  
Diagnosis Accuracy ◽  
Brain Tumor Diagnosis

Brain tumor is a deadly neurological disease caused by an abnormal and uncontrollable growth of cells inside the brain or skull. The mortality ratio of patients suffering from this disease is growing gradually. Analysing Magnetic Resonance Images (MRIs) manually is inadequate for efficient and accurate brain tumor diagnosis. An early diagnosis of the disease can activate a timely treatment consequently elevating the survival ratio of the patients. Modern brain imaging methodologies have augmented the detection ratio of brain tumor. In the past few years, a lot of research has been carried out for computer-aided diagnosis of human brain tumor to achieve 100% diagnosis accuracy. The focus of this research is on early diagnosis of brain tumor via Convolution Neural Network (CNN) to enhance state-of-the-art diagnosis accuracy. The proposed CNN is trained on a benchmark dataset, BR35H, containing brain tumor MRIs. The performance and sustainability of the model is evaluated on six different datasets, i.e., BMI-I, BTI, BMI-II, BTS, BMI-III, and BD-BT. To improve the performance of the model and to make it sustainable for totally unseen data, different geometric data augmentation techniques, along with statistical standardization, are employed. The proposed CNN-based CAD system for brain tumor diagnosis performs better than other systems by achieving an average accuracy of around 98.8% and a specificity of around 0.99. It also reveals 100% correct diagnosis for two brain MRI datasets, i.e., BTS and BD-BT. The performance of the proposed system is also compared with the other existing systems, and the analysis reveals that the proposed system outperforms all of them.

Smear technique in the intra-operative brain tumor diagnosis: Its advantages and limitations

1999 ◽  
Vol 21 (1) ◽  
pp. 121-124 ◽  
Author(s):  
J. Slowiński ◽  
M. Harabin-Slowińska ◽  
R. Mrówka
Keyword(s):  
Brain Tumor ◽  
Tumor Diagnosis ◽  
Brain Tumor Diagnosis

scholarly journals Cancer Imaging and Brain Tumor Diagnosis

2017 ◽  
Vol 09 (01) ◽  
Author(s):  
Hamidreza Shirzadfar ◽  
Samin Riahi ◽  
Mahsa Sadat Ghaziasgar
Keyword(s):  
Brain Tumor ◽  
Cancer Imaging ◽  
Tumor Diagnosis ◽  
Brain Tumor Diagnosis

Development of a real-time tactile sensing system for brain tumor diagnosis

2010 ◽  
Vol 5 (4) ◽  
pp. 359-367 ◽  
Author(s):  
Yoshihiro Tanaka ◽  
Qingyun Yu ◽  
Kazuki Doumoto ◽  
Akihito Sano ◽  
Yuichiro Hayashi ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Real Time ◽  
Tumor Diagnosis ◽  
Tactile Sensing ◽  
Sensing System ◽  
Brain Tumor Diagnosis
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