Brain abnormality detection from MRI of human head scans using the bilateral symmetry property and histogram similarity measures

Kalaiselvi, T.; Sriramakrishnan, P.; Somasundaram, K.

doi:10.1109/icsec.2016.7859867

Cited by 6 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results of the proposed method are also compared with various state‐of‐the‐art methods done on the WBA data set and are shown in Table 10. The performance of the proposed method is better when compared to existing methods such as the hybrid method, 26 predictive cognitive system for brain images (PCB) method, 27 Avinash's method, 28 wavelet method, 29 statistical method, 30 and Kalaiselvi's method 31 . The state‐of‐the‐art methods were developed using conventional image processing techniques but the proposed method has been developed using deep learning technique.…”

Section: Resultsmentioning

confidence: 93%

Development of automatic glioma brain tumor detection system using deep convolutional neural networks

Kalaiselvi

Padmapriya

Sriramakrishnan

et al. 2020

Int J Imaging Syst Tech

View full text Add to dashboard Cite

We have developed six convolutional neural network (CNN) models for finding optimal brain tumor detection system on high‐grade glioma and low‐grade glioma lesions from voluminous magnetic resonance imaging human brain scans. Glioma is the most common form of brain tumor. The models are constructed based on the different combinations and settings of hyperparameters with conventional CNN architecture. The six models are two layers with five epochs, five layers with dropout, five layers with stopping criteria (FLSC), FLSC and dropout (FLSCD), FLSC and batch normalization (FLSCBN), and FLSCBN and dropout. The models were trained and tested with BraTS2013 and whole brain atlas data sets. Among them, FLSCBN model yielded the best classification results for brain tumor detection. Experimental results revealed that our deep learning approach was better than the conventional state‐of‐art methods.

show abstract

Section: Resultsmentioning

confidence: 93%

Development of automatic glioma brain tumor detection system using deep convolutional neural networks

Kalaiselvi

Padmapriya

Sriramakrishnan

et al. 2020

Int J Imaging Syst Tech

View full text Add to dashboard Cite

show abstract

“…HSSIM suggested previously as SSIM scale cannot be well-implemented under signifcant noise. In (HSSIM) the researcher applied the common histogram and combined it with the conventional histogram as follows [18,19]:…”

Section: Histogram Similarity Measurementioning

confidence: 99%

A hybrid image similarity measure based on a new combination of different similarity techniques

Hamza¹,

Dihin²,

Abdulameer³

2020

IJECE

View full text Add to dashboard Cite

Image similarity is the degree of how two images are similar or dissimilar. It computes the similarity degree between the intensity patterns in images. A new image similarity measure named (HFEMM) is proposed in this paper. The HFEMM is composed of two phases. Phase 1, a modified histogram similarity measure (HSSIM) is merged with feature similarity measure (FSIM) to get a new measure called (HFM). In phase 2, the resulted (HFM) is merged with error measure (EMM) in order to get a new similarity measure, which is named (HFEMM). Different kindes of noises for example Gaussian, Uniform, and salt & ppepper noiser are used with the proposed methods. One of the human face databases (AT&T) is used in the experiments and random images are used as well. For the evaluation, the similarity percentage under peakk signal to noise ratio (PSNR) is usedd. To show the effectiveness of the proposed measure, a comparision anong different similar technique such as SSIM, HFM, EMM and HFEMM are considered. The proposed HFEMM achieved higher similarity result when PSNR was low compared to the other methods.

show abstract

Neural Network Optimizers for Brain Tumor Image Detection

Kalaiselvi

Padmapriya

2022

Applied Smart Health Care Informatics

View full text Add to dashboard Cite

Brain abnormality detection from MRI of human head scans using the bilateral symmetry property and histogram similarity measures

Cited by 6 publications

References 7 publications

Development of automatic glioma brain tumor detection system using deep convolutional neural networks

Development of automatic glioma brain tumor detection system using deep convolutional neural networks

A hybrid image similarity measure based on a new combination of different similarity techniques

Neural Network Optimizers for Brain Tumor Image Detection

Contact Info

Product

Resources

About