"Optimized CAHO-DNFN for MRI-Based Brain Tumour Classification"

What is it about?

A brain tumour is a deadly syndrome caused due to abnormal and uncontrolled expansion of extra cells that creates several tissues in the brain to affect the nervous system. It rapidly increases the growth of tumour cells and affects the brain by damaging or squeezing healthy tissues. Automatic brain tumour classification was done by conditional aquila horse herd optimization driven deep neuro fuzzy network (CAHO-based DNFN) based on MR image. First, image segmentation is done with a multi-encoder net framework (ME-Net), and features that involve statistical and convolutional neural network (CNN) features are extracted. Then, the ME-Net training is performed using AHO. Utilizing deep neuro fuzzy network (DNFN), which is trained by fusing CAViaR with AO and HOA, tumour classification is carried out utilizing augmented data after the process. The proposed scheme showed outstanding results with the measures, namely testing accuracy, specificity, and sensitivity that acquired the values of 0.915, 0.9 and 0.926, respectively

Featured Image

Why is it important?

The high testing accuracy, specificity, and sensitivity rates mentioned (91.5%, 90%, and 92.6%, respectively) indicate that this method can reliably distinguish between tumour and non-tumour regions in the brain. This precision is crucial for accurate diagnosis, which is the first step toward effective treatment.

Perspectives

“

With the advancement of AI in healthcare, ethical considerations such as patient privacy, data security, and the need for transparency in AI decision-making processes become increasingly important. Ensuring these technologies are developed and used responsibly is crucial.

”