Recent Trends on Brain Tumor Detection Using Hybrid Deep Learning Methods

The term "brain tumor" describes the unregulated increase in brain cells, which can have various adverse consequences.In the field of medical research, a variety of methods are employed to find brain tumor and the most reliable method still utilized by specialists is Magnetic Resonance Imaging (MRI).The noninvasive MRI method has developed into a primary emission brain tumor investigative tool.In order to accurately identify the extent of tumor, reliable, entirely an automatic segmentation method for the brain tumor and this is still being investigated.There is a higher possibility of success for the treatment when tumors are found early.Detecting brain tumor affected cells is tedious and time-consuming process.Identification and classification of brain tumors at the earliest is very essential for effective treatment.This article conducted an analysis of existing methodologies to apply various forms of deep learning techniques to MRI data.This review provides hybrid deep learning based brain tumor diagnosis approach which combines different deep learning methods like Convolutional Neural Networks (CNN), UNET Architecture, GoogLeNet and Gabor Filter for feature extraction.From extensive survey, this review concludes that deep learning approaches provide more accurate and efficient results than traditional machine learning algorithms.This survey highlights the current clinical challenges, potential future solutions and opens up the researcher's challenges to evolve systematic brain tumor detection system demonstrating clinically acceptable better accuracy which will assist the radiologists in diagnosis.

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