The hospital / health care?s need?s some intelligent system for diagnosing the disease from the x rays. If we have millions of x rays of people?s and we check the disease manually, it will consume so much time and energy. So, we have a problem solution that we can create a smart system that can diag
Autonomic Disease Diagnostics System for Chest Radiographs
The hospital / health care’s need’s some intelligent system for diagnosing the disease from the x rays. If we have millions of x rays of people’s and we check the disease manually, it will consume so much time and energy. So, we have a problem solution that we can create a smart system that can diagnose the disease. The machine can take x ray image as an input and then diagnose what’s the disease is, like: It’s Covid, Pneumonia, TB etc. This system can have used the technology of Deep Learning (DL) Model. We have disease classification model and we train the model with the help of x ray images. This system consists of Web Application for the hospital usage in which user can only browse the x ray image, the backend part should be cover with the Deep Learning (DL) Model.
We have an Objective that:
METHODOLOGY / IMPLEMENTATION OF PRODUCT:
Data Retrieval:
In the first phase, we can collect the data from the external websites they can provide the x ray images. Also we collect the data from the hospitals. We have too many files of the x ray images from the different sources.
Data Augmentation:
In this phase, if the collection of data is not enough for our model so, we can use the technique of augmentation in which we have one image and we create multiple images from one image like rotating the image, scaling the image and also duplicates from the four sides of the image because with this rotation process we have all four sided angle of the image that we will give to the Neural Network for the training that’s why we use this technique of augmentation. We have another advantage is that when we do this augmentation of data we increase the dataset from the limited data to more enough for our Model.
Testing a Model:
In this phase, after the completion of data cleaning process now we have the formatted data in our one file. Now, we created a CNN based Deep Learning Model in which we have two parts training and testing part we give the more data to training part and some data to testing part. After train our model we check the accuracy of our model. When if the model accuracy not good enough we use the different techniques to make our model efficient.
Web Application of Product:
In the final phase, we create the web based application for users in which we have the multiple pages that describes the detailed information about the disease and the symptoms of the disease. We have one practical page which contains the browse the image feature that user can select the chest x ray image from the computer and hit the button below our Deep Learning Model will execute from behind and they predict the disease with respect to the symptoms that the person has.
In the pandemic we have millions of patient’s chest x rays in the hospital. The problem raised that the doctors have to check the x rays manually to diagnose the disease. It’s will take too much time. All the hospital’s need’s some smart systems to check the x rays digitally. The important of this problem must increase because of the diseases. The Covid, Pneumonia and TB all have the chest x rays so, the doctors check them manually what’s the actual disease is that’s why they need’s some smart system that can predict what the actual disease is with the help of x ray images.
Phase I – V:
We use Jupyter Notebook as an editor.
Libraries for Phase I – V:
Numpy, Pandas, Matplotlib, Seaborn, Image, Sklearn, Preprocessing, model selection, tensorflow, keras, optimizers, OpenCV, Heroku etc.
Phase VI:
React JS, API, Firebase, Authentication etc.
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| License Software's, API'S, Hosting Services | Equipment | 5 | 9210 | 46050 |
| Total in (Rs) | 46050 |
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