Publication Date: 2023/12/01
Abstract: Brain tumor classification is a critical facet of medical diagnostics, influencing treatment decisions and patient outcomes. Traditional diagnostic methods often rely on manual interpretation of medical images, leading to challenges in accuracy and efficiency. This project introduces a revolutionary approach to brain tumor classification through the implementation of Convolutional Neural Networks (CNNs). The integration of CNNs, a subset of deep learning techniques, aims to enhance the accuracy, speed, and automation of brain tumor classification, marking a significant leap forward in medical image analysis. Brain tumors, both benign and malignant, present intricate challenges in terms of diagnosis and treatment planning. Existing diagnostic methods, while valuable, are often time-consuming and susceptible to interpretative variations. The motivation behind this project stems from the need for more robust, automated, and accurate diagnostic tools. By harnessing the power of CNNs, which have demonstrated remarkable success in image recognition tasks, we aim to address the limitations of traditional diagnostic approaches. The primary objective of this project is to develop a CNN-based model capable of accurately classifying brain tumors from medical images. This encompasses the identification of tumor types, differentiation between benign and malignant tumors, and providing a reliable tool for healthcare practitioners to expedite diagnosis and treatment planning.
Keywords: No Keywords Available
DOI: https://doi.org/10.5281/zenodo.10242815
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT23NOV1525.pdf
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