STREAMLINING BRAIN TUMOR DIAGNOSIS: KNOWLEDGE DISTILLATION FOR COMPUTATIONAL EFFICIENCY

Abstract

Brain tumors are biological conditions that are complex and need proper di agnosis at the earliest in order to receive medical treatments. Two limitations of the traditional diagnostics methods such as magnetic resonance imaging (MRI) analysis are time-consuming and computational expensive. In the following re gard, it is relevant to use knowledge distillation for accelerating the process of brain tumor diagnosis since it is facilitated through the transmission of the logit information from a large, very efficient model into a small, very effective model. The final distilled model is compared with the original model, and benchmark datasets are used for a competitive and quality comparison in terms of accuracy, computational cost, and inference time. This study demonstrates an interesting potential of KD to optimize treat ments for brain tumors, moving toward better and more widespread diagnosis. The database contains tumor and nontumor scans from the MRI model of the brain, which will ensure some appropriate way of value judgment. This is fol lowed by data preprocessing, with the MRI images at a quality and significance level to be fed into the model. Initially, a high-performing baseline could be set up through training a model with Convolutional Neural Network on the pre processed dataset. In this way, we get distillation by transferring the CNN knowl edge to some compact model so that high accuracy is maintained for reducing computational demand. This approach promises to make advanced diagnostic capacity more available, especially in resource-constrained settings, with an ulti mate improvement of patient outcomes realized through quicker and more efficient diagnoses.