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Brain Tumour Detection With Deep Learning In Python - Deep learning Project Help

Updated: Dec 6, 2022

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We have created a complete playlist of deep learning and deep learning projects videos with detailed explanation. In this video we have explained how to create a deep learning model. While building the deep learning project our developer takes care that you will learn from these videos a lot of things like how to prepare data for building the deep learning model etc.

In this article, we are talking about brain tumour detection models. Here we will give you complete information about the brain tumour detection model.

Doctors use various tests to find out or diagnose the brain tumour and learn the type of brain tumour. Image tests can help doctors find out if the tumour is in the primary stage or if it is cancer that has spread to the brain from elsewhere in the body. Image tests show the inside of the body and with the help of this doctor may consider these factors when choosing a diagnostic test.

Project Idea

The model brain tumour detection has to be trained using an image dataset that contains a lot of images of MRI (Magnetic Resonance imaging) data. With help of these images we build the deep learning model using convolutional neural networks. After that we evaluate the model and plot training accuracy and validation accuracy and training loss and validation loss.


To build the brain tumour detection model we have used MRI (Magnetic Resonance Imaging) data which is available on kaggle. The image data is already splitted into training and testing folders and each folder has more subfolders. Glioma tumour, meningioma tumour, no_tumour and pituitary tumour. The Glioma tumour folder contains 100 image files, meningioma tumour 115 images , no tumour 105 images and pituitary tumour has 74 image files.

In our explanation video of Data-driven brain tumour detection model using convolutional neural network, We use convolutional neural network in this project with best parameters possible for getting the best prediction accuracy. At last we evaluate the model by plotting training accuracy and loss and validation accuracy and loss.

The Brain tumour detection is described in two videos part 1 and part 2.

Part 1 : Title : BRAIN TUMOUR DETECTION Project Part 1 | AI ML Project Series

Description : This is the introduction part of BRAIN TUMOUR DETECTION Project where we provide the details and procedures of the coming project that we will build in Part2 of this Series. This is based on classification of an image to know if the MRI (Magnetic Resonance Imaging) image shows any signs of brain tumour or not and if yes, what kind of tumour. The result will be able to help the doctors to analyse the MRI (Magnetic Resonance Imaging) images to know the tumour state in the brain.

Part 1 Video Link

Part 2 : Title : BRAIN TUMOUR DETECTION Project Part 2 | AI ML Project Series

Description : This is the second part of the BRAIN TUMOUR DETECTION Project where we create a complete project on Kaggle Community Platform regarding classification of MRI images into types of Brain tumour or no tumour based on training data. We use Data directory flow, Convolutional Neural Network from Keras and Sequential Model along with OpenCV for creation of our model. As a result we will be able to predict accurately whether the MRI image shows any tumour or not.

We create a Neural Network based on VGG16 but has lower parameters and is faster due to the same reason.

Part 2 Video Link

Source Code


Kaggle platform

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