In this project, we develop a software system that allows the patient to register and make an appointment.
They can also upload an image of their operations to detect the tumor and analyze the image using machine learning algorithms and then classify them as benign and malignant after detecting the masses.
It is stored in the database under the name of the registered user.
This project was developed to assist doctors in hospitals in detecting cancer in mammographic masses.
The goal of the project
The project aims to detect cancer tumors in mammographic masses by using a deep learning application and sending them malignantly.
There will be a hospital system that allows databases on a website of patients mammographies.
The result of each treatment is the date of surgery, type of surgery, prescription of treatment disease, etc. Including his reason according to his profile.
2 Project Scope, Results and Program
The scope of our project is comprehensive for the patients and doctors in the hospital.
This system is responsible for requiring management and review between final management and hospital services.
Each usage profile contains name and surname, name of the surgery and other personal information such as the date of surgery, the doctor appointed for the surgery, the room number of the hospital and the address, phone number.
The patient must make an appointment before coming to the hospital.
. If there is free time for working hours with an existing doctor on the list, the patient will be assigned to this doctor at the given time, then the hospital is reported to be busy and arrive at the moment.
The data of each patient is stored in the database.
This data can be accessed by the administrator on our server or viewed as personal information (date of birth, working date, address, phone number, etc.) in each person's profile.
We want to divide our project into four sections.
In the first part, we will prepare the documentation of the system and bring more details and clarity on how to detect the tumor through deep learning and how to connect the front end (user) to the back end (server).
. In the second part, we will implement our system as a web application. We will connect to the server and host our website so that we can access it from anywhere.
After that, the connection with the database will be tested.
We need to make sure that every data can be updated at a certain time.
In the third part, we will take our system to the mobile side. We will choose a mobile framework.
At the final stage, we will test our web app and mobile app to look for hidden bugs.
Tested results from our samples will be collected in our documented results and at this stage we will use version control tools to manage our applications if there are any errors during the test (or when our system is running in the future).
Our project is dedicated to contributing to hospitals that aim to detect the tumor of their patients in mammography.
Users (patients) will need to be registered on the website and provide the necessary information.
We offer DL (deep learning) techniques to detect cancer in mammography.
Customers can check for an update in their recipe or data analysis.
While doctors can plan their time more efficiently.
For example, if something happens to a doctor, they may cancel their surgery in advance to have another doctor replaced.