- Colab file for blindness in 70 months prediction
- Blindness Deployed Code(NGROK)
- Last Working Local Proxy Link: Blindness Prognosis
- In case link doesn't work, follow these steps:
- Upload
coxnetTR.pklandcoxnetUT.pklmodel files to your drive - Import these model files and run the above colab file
- Upload
- Colab file for Diabetic retinopathy prediction
- Diabetic-Retinopathy-Prediction(NGROK)
- To run the ngrok file, create .h5 model from Colab file for Diabetic retinopathy prediction
- Then follow these steps:
- Upload
d5_weights.h5model files to your drive - Import these model files and run the above colab file
- Upload
Diabetes is a devastating health situation. Over 84% of sufferers are unaware that they've diabetes, and that is largely due to the fact there lacks a goal evaluation device for computerized detection. Diabetic Retinopathy is a eye situation that could purpose imaginative and prescient loss and blindness in human beings who've diabetes. Diabetic Retinopathy should be identified quickly and accurately, however, the inconsistency among clinicians exacerbates the cutting-edge negative remedy of DR. Currently, detecting DR is a totally time-ingesting task, which calls for an educated clinician to assess the retina. In addition to this, diagnosing sufferers may be extraordinarily inconsistent among ophthalmologists and our severity predictor guarantees a goal and brief evaluation of DR. To counter the adverse consequences of diabetic retinopathy and absence of consistency from clinician diagnosis, we present to you, the Visara.
Visara is a web-app that utilizes a variety of state-of-the-art models to revolutionize the ophthalmologic field. Owing to the current pandemic, travelling to gyms/yoga centers is neither safe nor feasible which brings our project to show. The web-app would not only predict the DR and the extent of blindness but would also suggest the Yoga postures which are proven to be beneficial in the cure of Diabetic Retinopathy using a Yoga Bot which detects the posture of the user while performing the Asana. Studies have shown that concise organization of reports leads to better efficiency of treatment. Additionally, doctors can often miss key information from their patient if a medical text report is disorganized so this summarizer ensures that no information is lost in the process of communicating with a doctor. Therefore based on the diagnosis results a report is generated which can be accessed both by the doctor and the patient for the evaluation.
Our Visara aims to bring a solution which can help people take necessary preventive measures against one of the most common problems faced by a diabetic person which is vision loss. The fundamental objective of the project is to allow patients and doctors to efficiently utilize resources and funds. The most attractive quality of this project is to try to automate almost everything from the detection of Diabetic Retinopathy (DR) severity prediction and blindness time prediction to creating a report summarizer and handing over the details to our YogaBot.
For new users, our YogaBot asks them a few generic questions regarding their medical history and recommends Yoga asanas to boost their health and immunity. For registered users, we keep a record of their previous asanas and their improvement to suggest some new routines. Our bot then acts as a personal trainer, records the body structure and notifies if the body is upheld correctly.
Our YogaBot is a recommendation feature which analyzes the user’s health to recommend and teach Asanas to boost their health structure. It acts as a personal trainer checking the posture of the user while performing the Asanas.
Diabetic Retinopathy (DR) severity prediction, blindness time prediction, and a report summarizer are DR’s three main features. Each of these features are displayed in the form of records, where doctors can add various types of records by selecting one of the 3 features. Once they fill out the necessary information required for each record, they can retrieve an output within seconds. All records can be conveniently displayed on a single page. Additionally, our app would implement a login/logout authentication system, which enables a user to easily and securely access their data.
-
For the DR severity prediction aspect of our web app, we will predict the stage of DR based on an image of the patient’s retina image. Our model classify the retina on a scale of 0-4, with 0 being no DR and 4 being the most severe.
-
The next section in our web app predicts the chance of a patient going blind from DR over a course of 70 months. A doctor can enter demographic and treatment information about the specific patient. Using a ML Model, we intend to create a graph with the percent chance of going blind over a course of months. This is helpful for the doctor and the patient as they can easily decide how soon they need treatment, and if they need treatment, without getting their retina scanned.
-
Lastly, we have a report summarizer for the doctor to easily view a summary of the patient’s condition write up. Similarly, patients can also view their doctor’s report in a more concise and organized format.
Over 84% of patients are unaware they have diabetes, and this is large because there lacks an objective assessment tool for automated detection. Currently, detecting DR is a very time-consuming task, which requires a trained clinician to evaluate the retina. In addition to this, diagnosing patients can be extremely inconsistent amongst ophthalmologists, and our severity predictor ensures an objective and quick assessment of DR. Moreover, we cater a personalized set of yoga asanas for each individual along with a personal posture trainer which comes without the risks of Covid transmission.
- HTML, CSS, JavaScript
- Python
- Tensorflow
- Flask
- OpenCV
- Natural Language Processing
- CNN
- Connect the patient with the doctor using a messaging feature.
- Diet Planner according to the patient stats.
- Keeping a track of patient exercise and diet history stats using IOT devices.
Our web app predicts whether a person will undergo the condition of Diabetic Retinopathy, if yes then what is the duration along with suggesting yoga asanas and connecting your reports with your preferred doctor to combat the disease naturally.
Diabetic retinopathy is the leading cause of blindness in working-age adults ages 20–74, also the whole covid and social distancing situation favors a healthcare model which is digital at its core similar to ours.
At the early stage apart from different digital marketing strategies our first aim would be to set up a good repo with the doctors who treat such patients by automating the process of detection and cure which will be the main point to attract these doctors to recommend or treat their patients through our platform.
Some orthodox doctors would cause a hindrance in making our platform attractive in the market but we are hopeful that technology will overcome stereotyping.
Since recent times forbid us to leave our houses this would be the best time to get a grip over a market where it so prevalent that around 93 million adults are affected by it on a global scale. According to a recently conducted survey, almost 63% of Indians are not even aware that diabetes has a hazardous effect on the eye apart from other body parts. 92% of diabetics underwent retinal analysis after having their vision affected. So the main goal would be to alert people at early stages so that they can take up necessary steps just like in the case of cancer where a person is able to save his/her vision to live a healthy life.
Ways to generate revenue from our system:
-
Create a customer-based service that gives them insights to keep them engaged with the platform.
-
Offer a service where a separate database is offered to a particular clinic so that they can monitor the situation of their clients and transfer information easily and save time for the doctor on unnecessary visits by the patients.
-
As we develop and optimize our product, we can provide a separate health consultancy service to help the clients stay fit through practicing yoga asanas.