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“Advancing Fake News Detection: Hybrid Deep Learning With FastText and Explainable AI” has been added to your cart. View cart
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Home Projects Python Brain Tumor Identification and Classification of MRI images using deep learning techniques
IoT based Smart Bin- A Swachh-Bharat Initiative
IoT based Smart Bin- A Swachh-Bharat Initiative ₹11,000.00
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Deep Convolutional Neural Network for Fire
Deep Convolutional Neural Network for Fire Detection ₹5,500.00

Brain Tumor Identification and Classification of MRI images using deep learning techniques

₹5,500.00

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SKU: Python - Deep Learning Categories: Deep Learning, Deep Learning, Projects, Python Tags: Brain Tumor, Convolutional Neural Network, Deep Learning, Flask Application, Image Processing, MRI Images, Python
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Description

Aim:

To detect and identify the Brain Tumor using Deep-Learning techniques

Ā Abstract:

Brain is the controlling unit of human body. It regulates the functions such as memory, vision, hearing, knowledge, personality, problem solving etc. The main reason for brain tumors is the uncontrolled development of brain cells. In medical practices, the early detection and recognition of brain tumors accurately is very vital. In literature, there are many techniques has been proposed by different researchers for the accurate segmentation of brain tumor. Magnetic resonance imaging (MRI) is high-quality medical imaging, particularly for brain imaging. MRI inside the human body is helpful to see the level of detail. The MRI is used even in diagnosis of most severe disease of medical science like brain tumors. The brain tumor detection process consist of image processing techniques involves four stages. Image pre-processing, image segmentation, feature extraction, and finally classification.

Proposed System

The diagnosis of tumors at the early stages is very important. Our proposed methodology is based on Deep Neural Network Model which trains on the Dataset and detects the image with a tumor and in such image the tumor gets segmented. Then we are using flask web framework to detect a tumor.

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