DEVELOPMENT OF WEB-BASED APPLICATION FOR TUMOR CLASSIFICATION USING PRE-TRAINED VGG19 MODELS - Indra Pramana, I Putu Rangga (Proceeding International Conference on Information Technology, Multimedia, Architecture, Design, and E-Business, 2024)
This paper discusses the development of a web-based application for brain tumor classification using a pre-trained VGG19 convolutional neural network (CNN) model. The application aims to assist in the early detection of brain tumors by automating the analysis of MRI images, thereby minimizing diagnostic errors. The dataset, sourced from Kaggle, consists of 253 brain MRI images, including 155 images of "Tumor" and 98 images of "No Tumor." The methodology involves transfer learning and fine-tuning of the VGG19 architecture to classify brain MRI images. The system is integrated into a user-friendly Flask-based web application, which allows healthcare professionals to access real-time predictions. Evaluation results show that the model achieves an accuracy of 82%, demonstrating its potential to assist doctors in making accurate diagnoses. This research addresses the critical need for reliable diagnostic tools, aiming to reduce misdiagnosis and improve patient outcomes. By leveraging deep learning and web-based platforms, the application offers a scalable solution for remote and automated brain tumor detection.