Brain Tumor Detection Using Python
Kamesh H. Nagpure
Department of Computer Application,
G H Raisoni Amravati University Nagpur, India
Received on: 17 June ,2024 Revised on: 19 July ,2024 Published on: 31 July ,2024
Abstract:
This study explores the application of machine learning techniques for the detection of brain tumors, focusing on various models’ performance and their potential to improve diagnostic accuracy. The research highlights the effectiveness of machine learning algorithms in analyzing medical images, identifying tumors with high precision, and offers insights into future advancements in this field. The findings contribute to the growing body of knowledge on AI in healthcare, providing practical recommendations for enhancing diagnostic tools.
Brain tumors pose significant challenges in medical diagnosis and treatment. Early detection is crucial for effective intervention and patient care. In this study, we propose a novel approach for brain tumor detection utilizing Python-based image processing techniques.
The proposed method involves preprocessing of MRI (Magnetic Resonance Imaging) images to enhance contrast and remove noise. Subsequently, feature extraction techniques are employed to capture relevant characteristics from the images. These features are then fed into a machine learning model, such as a Convolutional Neural Network (CNN), for classification.
Our approach leverages the power of Python libraries such as NumPy, OpenCV, and TensorFlow for efficient image manipulation, feature extraction, and model training. Additionally, we explore various CNN architectures to optimize the classification performance.
Keywords: Brain Tumor Detection, Machine Learning, Medical Imaging, Deep Learning, NumPy, OpenCV & TensorFlow
DOI LINK – https://doi.org/10.69758/GIMRJ2407II0IV12P0019
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