brain tumor detection using deep learning

  Brain tumor is a collection, or mass, of abnormal cells in your brain. Your skull encloses your brain, is very rigid. Any growth inside such a restricted space can cause problems. Brain tumor can be cancerous (malignant) or non-cancerous (benign).  According to the Natio

Project Title

brain tumor detection using deep learning

Project Area of Specialization

Artificial Intelligence

Project Summary

  Brain tumor is a collection, or mass, of abnormal cells in your brain. Your skull encloses your brain, is very rigid. Any growth inside such a restricted space can cause problems. Brain tumor can be cancerous (malignant) or non-cancerous (benign).  According to the National Brain Tumor Foundation (NBTF), 29,000 people are diagnosed with brain tumor in the United States (US) with brain tumor and 13,000 of those patients die per annum.

            Classification of Brain Tumor (BT) is a vital assignment for assessing Tumors and making a suitable treatment. There exist numerous imaging modalities that are utilized to identify tumors in the brain. Magnetic Resonance Imaging (MRI) is generally utilized for such a task because of its unrivaled quality of the image and the reality that it does not depend on ionizing radiations. The relevance of Artificial Intelligence (AI) in the form of Deep Learning (DL) in the area of medical imaging has paved the path to extraordinary developments in categorizing and detecting intricate pathological conditions, like a brain tumor, etc. Deep learning has demonstrated an astounding presentation, particularly in segmenting and classifying brain tumors. In this work, the AI-based classification of BT using Deep Learning Algorithms are proposed for the classifying types of brain tumors utilizing openly accessible datasets. These datasets classify BTs into (malignant and benign).

  Brain tumor is a collection, or mass, of abnormal cells in your brain. Your skull encloses your brain, is very rigid. Any growth inside such a restricted space can cause problems. Brain tumor can be cancerous (malignant) or non-cancerous (benign).  According to the National Brain Tumor Foundation (NBTF), 29,000 people are diagnosed with brain tumor in the United States (US) with brain tumor and 13,000 of those patients die per annum.

            Classification of Brain Tumor (BT) is a vital assignment for assessing Tumors and making a suitable treatment. There exist numerous imaging modalities that are utilized to identify tumors in the brain. Magnetic Resonance Imaging (MRI) is generally utilized for such a task because of its unrivaled quality of the image and the reality that it does not depend on ionizing radiations. The relevance of Artificial Intelligence (AI) in the form of Deep Learning (DL) in the area of medical imaging has paved the path to extraordinary developments in categorizing and detecting intricate pathological conditions, like a brain tumor, etc. Deep learning has demonstrated an astounding presentation, particularly in segmenting and classifying brain tumors. In this work, the AI-based classification of BT using Deep Learning Algorithms are proposed for the classifying types of brain tumors utilizing openly accessible datasets. These datasets classify BTs into (malignant and benign).

Project Objectives

• The objective of this investigation is to enhance the exactness of brain MR image identification by using DL algorithms and Transfer Learning (TL) approach. TL is the assignment of utilizing the information given by a pre-trained system to learn new models provided by new data.

• Collaborating a pre-trained system with TL is usually a lot quicker and simpler than starting from basic.
• If the tumor is diagnosed and treated early in the tumor formation process, the chance of patient’s recovery is very high. Therefore, the treatment of patient depends on the timely diagnosis of the tumor.
• We propose the system that will detect brain tumors with more accuracy, even if the tumor is small in size. Our proposed system works more accurately and efficiently than others with an MRI image even if it is a noisy data.

Project Implementation Method

• We here investigate the five unique DL models like Alex Net, Google Net, ResNet50, ResNet101, and Squeeze Net utilizing MR images of BT and apply TL techniques on the given dataset.
• Such pre trained CNN models are utilized to perform TL to extricate features that are visually distinguishable and essential. Finally, the classification of these features is done utilizing the soft max layer.
• It begins with the MR brain images dataset that was gathered and arranged into benign and malignant MR slices.
• The proposed strategy contains the accompanying stages: pre-processing, data division (cross validation), and augmentation, DL based extraction of features, lastly the tumor type classification.

Benefits of the Project

Benefits of Project:

• There are possibilities of errors in manual system of examination of MRI images so we need of autonomous system that will minimize the chance of errors.
• It will also minimize time consumption the system needed to process the MRI images within a few second and produce results.
• This system will also ease the surgery of brain tumor because system will provide (reference) exact location of brain tumors so that only specific part will be operated.
•  Our proposed system will provide high accuracy to identify the tumors because no multiple MRI images readings are required.
• Reduce Hospital costs – less money spent on consulting the doctors.
• It is easy to backup.
•  Quick and easy updates.
•  Reach anybody, anywhere in the world.
• Available 24 hours a day, 7 days a week.
• Direct access to latest information.
• Provide a user interface.
• To serve the patient with best possible quality and maximum facilities with less price.
• Ensures safety of user data.
• Ensures user satisfaction.

Technical Details of Final Deliverable

Technical detail:

• Hardware Requirements:

• We need a GPU (8GB) and an SSD to perform more accurate training and testing process.

• System Features:

• Upload Magnetic Resonance Imaging (MRI) images
• Read MRI images
• Extract Magnetic Resonance Imaging (MRI) images
• Network Training and testing
• Apply deep learning on Magnetic Resonance Imaging (MRI) images

• User inter phase:

• login the account
• Upload the MRI Image for brain tumor detection
• Collect the result

Final Deliverable of the Project

Software System

Core Industry

IT

Other Industries

Medical

Core Technology

Artificial Intelligence(AI)

Other Technologies

Others

Sustainable Development Goals

Good Health and Well-Being for People, Industry, Innovation and Infrastructure

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com