Alpha Lab With Brain Tumor Detection

Project Title

Alpha Lab With Brain Tumor Detection

Project Area of Specialization Artificial IntelligenceProject Summary

Brain Tumor segmentation is one of the most crucial and arduous tasks in the terrain of medical image processing as a human-assisted manual classification can result in inaccurate prediction and diagnosis. Moreover, it is an aggravating task when there is a large amount of data present to be assisted. Brain tumors have high diversity in appearance and there is a similarity between tumor and normal tissues and thus the extraction of tumor regions from images becomes unyielding. In this paper, we proposed a method to extract brain tumor from Magnetic Resonance brain Images (MRI) by CNN (Convolution Neural Network) Vgg16 which is not followed by traditional classifiers and convolutional neural network. The experimental study was carried on a real-time dataset with diverse tumor sizes, locations, shapes, and different image intensities. In traditional classifier part, we applied six traditional classifiers namely Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Multilayer Perceptron (MLP), Logistic Regression, Naïve Bayes and Random Forest which was implemented in sick it

Project Objectives

In today’s digital era, capturing, storing and analysis of medical image had been digitized. Even with state-of-the-art techniques, detailed interpretation of medical image is a challenge from the perspective of time and accuracy. The challenge stands tall especially in regions with abnormal color and shape which needs to be identified by radiologists for future studies. This project is to develop a program using deep learning model that will detect the presence of tumor with other details like the size and type of the tumor through an MRI scan and provide the result to the user. The accurate results will also help the doctors in the treatment process. The goal of the system will be to distinguish between tumor affected and non- affected brains. Another objective of this project is to develop a hybrid blood testing application through which the patient will be able to set an appointment with the lab and the lab will send someone to patient’s house to collect their blood sample for them and they will also be able to check the report through the app, this way the patient did not have to go to the lab to give blood sample. The brain tumor detection system will also be integrated with the same app that way the user can also detect the tumor with this app. Hence the main purpose of this project is to use a systematic approach to create a convenient and professional way to solve the mentioned problems.

Project Implementation Method

Brain Tumor Detection is the use of artificial intelligence to detect brain tumors. It is a subset of medical imaging, and it is done by using computer algorithms to analyze medical images. The goal of this research is to create an algorithm that will be able to detect brain tumors with an accuracy rate of over 90%. Brain tumor detection has traditionally been done through a systematic approach, but this new research will be using advanced deep learning algorithm of CNN and VGG-16. The Alpha Lab is a mobile app that helps patients and doctors to detect tumors in the brain. The app is designed to be userfriendly and easy to use. It provides an interface where patients can easily book their appointments, share their results with doctors, and collect their test data. The app’s secondary function is to provide an online blood test service with the help of which people can get their blood tested by a lab without having to go through any hassle. The results of the tests are available on the app itself, so you don’t need to wait for days for your doctor to call you up with your results.

METHADOLOGY: PLANNING: The Purpose of this first phase to find out the scope of the problem and determine solutions. Resources, costs, time, benefits and other items should be considered here DESIGN IMPLEMENTATION After requirements analysis, the next phase of the project will be to build an initial design of the system. It won’t be detailed like an actual design, but it contains all the important aspects of the system. The design phase describes how the system will operate, in terms of hardware, software, and network infrastructure. The main purpose of creating this preliminary design is to verify that a specific design meets our requirements. First, we build the GUI Of both mobile and computer application

Benefits of the Project

Our train model of CNN can also be used in Automated Surgeries of Brain tumor like laser surgery or something similar to it in future. The AI automated surgery machine will use this model for treatment of brain tumor cause the model will perfectly identify a tumor in MRI scan Dicom file of a brain tumor patient and segment the area of tumor for the surgery, it can also use by doctors and patients in order diagnose the tumor and suspect it in early stage. In future aspects there can be more areas where it can be used depends on the advancement of medical science and Technology.

Technical Details of Final Deliverable

Image classification plays a significant role in medical image processing as medical images have different diversities. For brain tumor classification, we used MRI and CT scan images. MRI is most vastly used for brain tumor segmentation and classification. In our work, we will be using a pre trained model VGG-16 for tumor classification which can predict tumor cells accurately. The segmentation process will be followed by classification using Convolutional Neural Network. We will present a fully automatic brain tumor detection and classification method using VGG-16 based deep convolution network, we will demonstrate that our method can provide both efficient and robust classification compared to the manual delineated ground truth. The proposed method makes it possible to generate a patient-specific brain tumor classification model without manual interference, and this potentially enables objective lesion assessment for clinical tasks such as diagnosis, treatment planning and patient monitoring. With the large dataset we can improve its prediction and able to get more accuracy which can help Patient and Doctors. To conclude, we have successfully created our project and mobile application as well of our project that is yet in its beta build phase and many quality-of-life improvements will be made as to increase its quality.

In our Mobile Application and Web Application we have used Codefirst Approach and Entity Framework of .NET as well. And for the main future of AI BRAIN TUMOR DETECTION Model, we implemented VGG-16 Algorithm which is a pre-trained model through which we can achieve the accuracy to the maximum level.

Final Deliverable of the Project Software SystemCore Industry MedicalOther IndustriesCore Technology Artificial Intelligence(AI)Other TechnologiesSustainable Development Goals Good Health and Well-Being for PeopleRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	52400
NVIDIA GTX 760 3GB GDDR5 192-bit GRAPHICS CARD 2XDVI/DP/HDMI	Equipment	1	26000	26000
VPS for APIs	Miscellaneous	1	7500	7500
Professional subscription of visual studio	Equipment	1	8700	8700
Hosting server for python	Equipment	1	7800	7800
Documentation Printing	Miscellaneous	2	1200	2400