Brain Tumor Detection System Using Deep Learning

• Author(s): Joseph Jeremiah Adekunle ; Adebanwo Hassan Adebola ; Princess Beatrice Sunday-Jimmy ; Onyekachi Innocent Ike-Okpe ; Abiodun Wisdom Oshireku
• Paper ID: 1706029
• Page: 151-161
• Published Date: 13-07-2024
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 8 Issue 1 July-2024

Abstract

Brain tumors significantly threaten human health, necessitating accurate and timely diagnosis to improve patient outcomes. Traditional diagnostic methods, such as manual analysis of MRI scans, are time-consuming, labor-intensive, and prone to human error. These limitations highlight the urgent need for more advanced, efficient, and reliable detection techniques. This article explores the implementation of deep learning, specifically convolutional neural networks (CNNs), in the detection of brain tumors. Deep learning models, which automate feature extraction and handle high-dimensional data, offer substantial improvements over traditional methods. We discuss the various layers of CNNs, and RNNs and their roles in processing and classifying medical images. By utilizing large datasets and transfer learning, these models can learn complex patterns and generalize well to new data, enhancing diagnostic accuracy and speed. The integration of deep learning in clinical settings can mitigate the challenges of traditional methods, such as high costs and the invasiveness of biopsies, leading to better patient care. Furthermore, the article emphasizes the importance of investing in data infrastructure, training healthcare professionals, and fostering research collaborations to advance the field. Regulatory and ethical considerations are also crucial to ensure the responsible and transparent use of AI in healthcare. In conclusion, the adoption of deep learning for brain tumor detection promises a significant leap forward in diagnostic capabilities, ultimately improving patient outcomes and setting a higher standard for medical care.

Keywords

Brain Tumor, Magnetic Resonance Imaging (MRI), Artificial Intelligence (AI), Machine Learning (ML), Deep Learning Algorithms, Convolutional Neural Network (CNN.

Citations

IRE Journals:
Joseph Jeremiah Adekunle , Adebanwo Hassan Adebola , Princess Beatrice Sunday-Jimmy , Onyekachi Innocent Ike-Okpe , Abiodun Wisdom Oshireku "Brain Tumor Detection System Using Deep Learning" Iconic Research And Engineering Journals Volume 8 Issue 1 2024 Page 151-161

IEEE:
Joseph Jeremiah Adekunle , Adebanwo Hassan Adebola , Princess Beatrice Sunday-Jimmy , Onyekachi Innocent Ike-Okpe , Abiodun Wisdom Oshireku "Brain Tumor Detection System Using Deep Learning" Iconic Research And Engineering Journals, 8(1)