Brain MRI Images Classifications with Deep Fuzzy Clustering and Deep Residual Network

What is it about?

The brain tumor is the most serious cancer among people of all ages, and recognition of its grade is a complex task for monitoring health. In addition, the earlier detection and classification of tumors into a particular grade are imperative for diagnosing the tumor effectively. This paper devises a novel method for multigrade tumor classification using deep architecture. First, the pre-processing is performed with the Region of interest (ROI) and Type 2 Fuzzy and Cuckoo Search (T2FCS) filter. After that, segmentation using a pre-processed image is carried out to generate segments, which is performed using a deep fuzzy clustering model. Then, the significant features are mined through segments that involve convolution neural network (CNN) features, Texton features, EMD features, and statistical features such as mean, variance, kurtosis, and entropy. The obtained features are subjected to Deep Residual Network for multigrade tumor classification. The Deep Residual Network training is done with the proposed Harmony search-based Feedback Artificial Tree (HSFAT) algorithm. The proposed HSFAT is devised by combining Harmony search and Feedback Artificial Tree (FAT) algorithm. The proposed HSFAT-based deep residual network provided superior performance with maximum accuracy of 94.33%, maximum sensitivity of 97.27%, and maximum specificity of 92.61%.

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Why is it important?

Brain tumors are one of the most serious cancers affecting people of all ages. Given their location and potential impact on neurological functions, timely and accurate diagnosis is crucial for effective treatment and management. Recognizing the grade of a brain tumor is a complex task due to the varying levels of aggressiveness and potential for progression. Differentiating between tumor grades accurately is essential for determining the appropriate treatment strategy and assessing prognosis.

Perspectives

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The developed method holds promise for clinical applications in tumor diagnosis and grading. Accurate and efficient multigrade tumor classification can aid healthcare professionals in making informed decisions regarding treatment strategies and patient management. Future research directions may involve validating the proposed method on larger datasets and exploring additional optimization techniques for further improving classification accuracy. Additionally, the method could be extended to other medical imaging modalities for broader applicability in oncology.

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