Abstract

In recent years in medical imaging technology, the advancement for medical diagnosis, the initial assessment of the ailment, and the abnormality have become challenging for radiologists. Magnetic resonance imaging is one such predominant technology used extensively for the initial evaluation of ailments. The primary goal is to mechanizean approach that can accurately assess the damaged region of the human brain throughan automated segmentation process that requires minimal training and can learn by itself from the previous experimental outcomes. It is computationally more efficient than other supervised learning strategies such as CNN deep learning models. As a result, the process of investigation and statistical analysis of the abnormality would be made much more comfortable and convenient. The proposed approach’s performance seems to be much better compared to its counterparts, with an accuracy of 77% with minimal training of the model. Furthermore, the performance of the proposed training model is evaluated through various performance evaluation metrics like sensitivity, specificity, the Jaccard Similarity Index, and the Matthews correlation coefficient, where the proposed model is productive with minimal training.

Highlights

  • Medical imaging technology has been critical in medical diagnostics for accurately detecting the presence of malignant tissues in the human body

  • This paper has proposed an adaptive structural similarity index that would assess the membership alongside the similarity index to make the outcome more realistic

  • The experimentation has been carried forward over the real-time MR images acquired from the open-source repository LGG dataset acquired from The Cancer Genome Atlas (TCGA) captured from patients with acute glioma

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Summary

Introduction

Medical imaging technology has been critical in medical diagnostics for accurately detecting the presence of malignant tissues in the human body. Self-Learning Network-based segmentation for real-time brain M.R. images through HARIS.

Results
Conclusion

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