Brain tumors are one of the most serious and life-threatening diseases in infants. Early and accurate diagnosis of brain tumors is crucial for choosing the optimal treatment and improving the survival rate of patients. Magnetic resonance imaging (MRI) is a widely used technique for detecting and analyzing brain tumors. However, manual interpretation of MRI images is time-consuming, subjective, and prone to errors. Therefore, there is a need for an automated and reliable system for brain tumor detection. In this paper, we propose a novel method for infant brain tumor detection using recurrent neural networks (RNN). RNN is a type of deep learning model that can capture the temporal and spatial dependencies in sequential data. We use RNN to process the MRI slices of infant brains and extract the features that are relevant for tumor detection. We then use a classifier to predict the presence or absence of a tumor in each slice. We evaluate our method on a dataset of infant brain MRI images and compare it with existing methods based on convolutional neural networks (CNN) and support vector machines (SVM). Our results show that our method achieves higher accuracy, sensitivity, and specificity than the state-of-the-art methods. We also demonstrate that our method can handle different types of tumors, such as glioma, meningioma, and medulloblastoma, and different stages of tumor development. Our method can be a useful tool for assisting radiologists and neurologists in the diagnosis of infant brain tumors.


  • Malignant brain tumors are one of the leading causes of death in adults and children.
  • To identify a brain tumor, an MRI image is acquired and analyzed manually by an expert to find lesions.
  • In Existing system, present two methods for the detection of brain tumors in medical images.
  • The first is based on Deep Learning through the U-net architecture that has proven its robustness vis-à-vis the segmentation of images, especially medical images.


  • In the existing Detection approaches, the multiresolution fusion approaches have been widely used in the recent studies because of their poor efficiency and inconvenience and their fusion results are usually limited by the number of decomposition layer and the selection of fusion rules.
  • Also distort the spectral characteristics with different degree.
  • A detailed study indicated that the color distortion problem arises from the change of the saturation during the fusion process.


  • Deep learning (DL) enables a pre-trained Convolutional Neural Network (CNN) model for medical images. CNN-based classifier systems are fully automated and do not require manually segmented tumor regions.
    Some researchers have proposed different CNN architectures. Most of the CNN models reported multiclass brain tumor detection.
    We have developed a hybrid model based on CNN for classifying the tumor type in the brain.
  • We have proposed an automated brain tumor detection mechanism applying CNN with transfer learning models on the Ultrasound brain image dataset
  • The dataset contains 2 classes: yes and no which contains 2533 Brain Ultrasound  Images. The class yes contains 1155 Brain MRI Images that are tumorous and the  class no contains 980 Brain MRI Images that are non-tumorous. Since this is a  small dataset, we can use data augmentation in order to create more images.
  • We can also get rid of imbalanced data as 61% of previous data contains yes  hence data augmentation will also solve this problem


  • The proposed image fusion has several advantages in the field of digital image processing.
  • The useful information can be gathered into a single image from multiple images which reduce the time complexity of processing.
  • The image fusion improves the reliability of the resultant image by grouping the redundant information into a single image.
  • The capability of the image can be improved by having the complementary information in the fused image.



Software Requirements:

Front End – Anaconda IDE
Backend – SQL
Language – Python 3.8
Hardware Requirements

•Hard Disk: Greater than 500 GB
•RAM: Greater than 4 GB
•Processor: I3 and Above

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* Complete Presentation Slides
* Flow Diagram
* Database File
* Screenshots
* Execution Procedure
* Readme File
* Addons
* Video Tutorials
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