In this chapter, we provide a summary of the main contributions of the study, reflect on the challenges and opportunities of developing a deep learning model and an Android app for brain tumor detection using MRI images, discuss the limitations of the study and potential future research directions, and conclude with implications for medical ...

Use case diagrams are used to graphically describe the functions provided by the system based on participants, their goals, and any dependencies between these use cases.

We employ a variety of machine learning techniques, including support vector machines (SVM), decision trees, and deep learning models, to efficiently identify and categorize stroke cases from medical imaging data.

This project describes how to use deep learning (CNN) to detect brain tumor in medical images, solving the problem of tumor differentiation and unraveling the complexity of the distributed grid. Four prominent CNN architectures and two additional models (MobileNet) are assessed for their performance in brain tumor classification.

A systematic approach for MRI brain tumor localization and segmentation using deep learning and active contouring. J Healthcare Eng. 2021 doi: 10.1155/2021/6695108.

Aug 3, 2019 · Performance analysis of automated brain tumor detection from MR imaging and CT scan using basic image processing techniques based on various hard and soft computing has been performed in our work. Moreover, we applied six …

This project aims to develop an accurate and efficient system for detecting brain tumors using Convolutional Neural Networks (CNN). Utilizing deep learning techniques, the model is trained on a dataset of brain MRI images, which are categorized into two classes: healthy and tumor.

Dec 27, 2023 · In this study, we employ a transfer learning-based fine-tuning approach using EfficientNets to classify brain tumors into three categories: glioma, meningioma, and pituitary tumors.

This project aims to revolutionize the early detection of brain hemorrhages in medical images, addressing the challenge faced by radiologists in identifying subtle symptoms. Through the application of deep learning, specifically convolutional neural networks (CNNs), we navigate the scarcity of annotated medical data using transfer learning.