Brain Tumor Classification Using MobileNet
Medapati Prema Kumar, Dodda Hasmitha, B A Usha, Bolla Jyothsna, Digupati Sravya
Abstract
One of the deadliest diseases is a brain tumor, which develops when brain tissue inside the skull grows suddenly and uncontrollably. Brain tumors are a significant health concern worldwide, necessitating timely and accurate diagnosis for effective treatment. Due to advancements in deep learning techniques to automatically identify brain cancers using MRI data. Here the MobileNet model is used because of its efficiency and effectiveness, it is a lightweight deep learning model architecture, which has made it a viable contender for medical image processing tasks. The presented methodology involves three main phases: Pre-processing, Feature extraction, and lastly detection of the tumor. Here MobileNet is used to automatically extract discrimination features from preprocessed images. Subsequently, the model was trained using the dataset, tested, and compared with other deep learning methods. The results showed that the MobileNet model accurately detects cancers, allowing for timely treatment to prevent any physical aftereffects like paralysis and other issues.