Deep learning based Brain Tumour Classification based on Recursive Sigmoid Neural Network based on Multi-Scale Neural Segmentation

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Basic diagram for Brain tumour classification
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Published: Jan 31, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i2s.6031
Keywords:
Brain tumour, Recursive Sigmoid Neural Network based on Multi-scale Neural Segmentation (RSN2-MSNS), colour histogram, Enhanced Deep Clustering U-net (EDCU-net), segmenting, Convolution Deep Feature Spectral Similarity (CDFS2), classification, train and testing parameters

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S. Syed Ibrahim
Research Scholar, PG and Research Department of Computer Science, Jamal Mohamed College (Autonomous) (Affiliated to Bharathidasan University), Tiruchirapalli, Tamilnadu, India
G. Ravi
Associate Professor and Head, PG and Research Department of Computer Science, Jamal Mohamed College (Autonomous) (Affiliated to Bharathidasan University), Tiruchirapalli, Tamilnadu, India

Abstract

Brain tumours are malignant tissues in which cells replicate rapidly and indefinitely, and tumours grow out of control. Deep learning has the potential to overcome challenges associated with brain tumour diagnosis and intervention. It is well known that segmentation methods can be used to remove abnormal tumour areas in the brain. It is one of the advanced technology classification and detection tools. Can effectively achieve early diagnosis of the disease or brain tumours through reliable and advanced neural network classification algorithms. Previous algorithm has some drawbacks, an automatic and reliable method for segmentation is needed. However, the large spatial and structural heterogeneity between brain tumors makes automated segmentation a challenging problem. Image tumors have irregular shapes and are spatially located in any part of the brain, making their segmentation is inaccurate for clinical purposes a challenging task. In this work, propose a method Recursive SigmoidNeural Network based on Multi-scale Neural Segmentation (RSN2-MSNS) for image proper segmentation. Initially collets the image dataset from standard repository for brain tumour classification.  Next, pre-processing method that targets only a small part of an image rather than the entire image. This approach reduces computational time and overcomes the over complication. Second stage, segmenting the images based on the Enhanced Deep Clustering U-net (EDCU-net) for estimating the boundary points in the brain tumour images. This method can successfully colour histogram values are evaluating segment complex images that contain both textured and non-textured regions. Third stage, Feature extraction for extracts the features from segmenting images using Convolution Deep Feature Spectral Similarity (CDFS2) scaled the values from images extracting the relevant weights based on its threshold limits. Then selecting the features from extracting stage, this selection is based on the relational weights. And finally classified the features based on the Recursive Sigmoid Neural Network based on Multi-scale Neural Segmentation (RSN2-MSNS) for evaluating the proposed brain tumour classification model consists of 1500 trainable images and the proposed method achieves 97.0% accuracy. The sensitivity, specificity, detection accuracy and F1 measures were 96.4%, 952%, and 95.9%, respectively.

Article Details

How to Cite
Ibrahim, S. S. ., & Ravi, G. . (2023). Deep learning based Brain Tumour Classification based on Recursive Sigmoid Neural Network based on Multi-Scale Neural Segmentation. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2s), 77–86. https://doi.org/10.17762/ijritcc.v11i2s.6031
Issue
Vol. 11 No. 2s (2023)
Section
Articles

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