Novel Approach for Brain Tumor Classification with CNN and MobileNet

Brain cancer remains one of the deadliest illnesses today, boasting an alarming mortality rate. Brain tumors arise from abnormal proliferation of cells; categorizing their shape, orientation and location poses a formidable challenge to medical imaging technology; among the various methods used for analysis magnetic resonance imaging (MRI) has proven especially successful. Regrettably, this method may lead to algorithmic execution errors and decrease overall algorithmic efficiency. A key component of its invention is the way it implements a CNN-Based multiscale feature extraction model, which is an end-to-end convolutional neural network made especially for the identification of brain tumors. By accurately and consistently classifying extracted features through lengthy training across several datasets, MobileNet-V2 models are capable of categorization. The advantage of this proposed approach gives more robust and accurate framework for classifying brain tumors with CNN-Based multiscale feature extraction model and the integration of the MobileNet-V2 model classification. This delivers promising improvements in medical imaging with regard to the analysis of brain tumors.