Year: 2024
Type: article
Abstract: Tumor of the brain is one of the most severe pathological identification. whose patients depend on early detection and enough treatment. Consequently, in the current practice, it is common to obtain MRI scans accompanied by assessment by radiologists could become boring and could contain mistake. Presently, with the help of ML and the recent pioneering findings in the imaging. techniques there is a tremendous chance of enhancing the enhancement of the speed and accuracy of detection of brain tumours. This work uses a dataset obtained from Kaggle to contain images of healthy TCs from healthy brains, brains with tumors and also applies a MobileNetV2 deep But in case of online learning, the model to be used is a polymer of CNN learning model to act as an automated. detection. Google Colab is used for computation while Gradio for explaining the results of the model trained. used for implementing the userinteractive interface through where users can enter scans of their brain to get the predictions from. The intent of the system in this approach is to present a sensible and easy-to-use telecommunication friendly system that can effectively early screen for AD. brain tumors. Keywords Neuro-oncology, Screening, Gradio, MobileNetV2, Google Colab,Deep Learning Models,Convolutional Neural Networks(CNN).
DOI: 10.55041/ijsrem40329
Authors: Assistant Professor Dept of Electronics and Communication Engineering, Kiran Kumar Humse, Sanjana A, Shashank H P, Vaishnavi K, Vinay M
Language: English
Subfield: Neurology
Field: Neuroscience
Domain: Life Sciences
Open Access status: bronze
OpenAlex ID: W4405948602