This is a project that I have made using CNN and web development. This project can detect the type of medical waste along with the suitable color bin and some relevant information about its disposal.

1. Medical
waste
segregation
A deep learning
based approach for
effective waste
disposal

2. Introduction
 Waste segregation is the process by which waste is separated into different
elements.
 This process can be both manual and automatic.
 The manual method is to collect the wastes from household and then send it to
collection sites of individual types.
 The automated method is to separate the wastes in recovery facilities or in
biological treatment systems.
 Often the automated method fails and it becomes tedious to segregate large
amounts of waste.
 Hence this process will be efficient if the segregation has been done in the earlier
phase (i.e. segregation in household itself..)

3. Importance of waste segregation
 When not segregated, the disposal of wastes becomes a difficult task and may
pose serious environmental issues.
 Segregation is the most crucial step in bio-medical waste management. Effective
segregation alone results in 70-80% of efficacy in biomedical waste
management.
 It is essential that every facility should separate its waste at the source to reduce
risk of infection, as well as the cost of handling and disposal.
 At each point of waste generation, there should be separate, properly labelled and
colour-coded containers appropriate for the specified type of waste.

4. Need for waste segregation
 The following census about the medical waste generation was known from the
Central Board of Pollution.
 There are only 200+ medical waste treatment facilities around the country to
grapple with this waste.
 Also setting up a treatment center in this country is not economically feasible as
mentioned by the Common Biomedical Waste Treatment Facility.
YEAR WASTE IN TPD (TONNES PER DAY)
2016 520
2018 550
2020 780

5. Household Medical Waste
 All these were with respect to hospitals, the main problem is in household.
 Most of the medical diagnosis being made automated, the amount of medical
waste generated in households have increased three fold by 2020.
 Around 78% of medical waste has been found mixed with that of general waste.
 Hence it is essential that the segregation has to be started from households.
 Also the waste collection sites are less in India and the awareness about this is
minimal.

6. Objectives
 The prime objective is to segregate the waste based on its image and also
provide an effective way for its disposal.
 To develop a dataset that contains various images of possible medical wastes
which have been categorized into three major groups. (Chemical, General &
Sharp).
 To develop an algorithm for the image processing and classification.
 To train, test and evaluate the performance of the model on real-world images.
 To develop a website, application or any portal to implement the trained
model.

7. Components Used
 Convolutional Neural Networks (CNN) are a specific application of deep learning in the
field of images. This network is used to classify or perform operations on images.
 MobileNetV2 is one such deep CNN which was developed by the Google for its low
memory. In this project this model has been used for the waste segregation.
 TensorFlow keras is such an library found in python well suited for CNN’s. the pre-trained
model was available in this library. Apart from that another function called
ImageDataGenerator was available which can generate samples out of images.
 The streamlit was used to develop a web application design format from the developed
code.
 The pyngrok was used to host a website and create URL for the web application.

8. PROCEDURE
1. Uploading the dataset into the IDE.
2. Providing the path for the training and testing image datasets.
3. Applying image processing and augmentation using the ImageDatagenerator.
4. Importing MobileNetV2 from TensorFlow keras library.
5. Using the layer.trainable= false through which we can significantly reduce the
parameters to be trained. As a result of this step only 3.2% of the entire parameters
have to be trained.
6. Changing the output classes to 3 and providing softmax activation function (softmax
provides a distribution of probability and is recommended for multiclass classification).
7. Compiling the model using cross entropy loss function and Adam optimising algorithm.

9. Continued..
9. Training the model for the desired number of steps and epochs.
10. Saving the model with the highest accuracy.
11. Importing streamlit and required libraries.
12. Creating the design for the website and loading the model.
13. Importing the stable ngrok zip file.
14. Importing the https from ngrok
15. Combining the https from the obtained URL & Running the
application on the website.

10. Output
 The final output is the prediction of the model as one of the four groups of
medical waste.
 Along with that the appropriate color code bin for the predicted waste is
displayed.
 Some relevant information regarding the waste is also provided just to create some
awareness to the user regarding the process of segregation and disposal.
TYPE OF WASTE RESPECTIVE COLOR BIN
Chemical Yellow
General Green
Sharp White

12. Advantages
 The model has achieved 99% training accuracy and 86% validation accuracy.
 An effective website has been developed for the users to access instead of looking
into the codes and getting confused.
 The model provides the color bin and also relevant information apart from the
detected type of medical waste.
 This project can improvise the process of medical waste segregation especially
from the household perspective.
 The awareness about the medical collection sites can be increased through
this project.

13. Disadvantages
 The images for which the model has been trained are taken from websites like
Google, Shutterstock etc. the model has to be trained on real-world images which
takes time and effort.
 The model has performed well for small-time images but this would require a lot
more data if to be deployed.
 There are cases where the model has failed to categorize the images properly. For
instance a broken thermometer is a sharp waste and also a chemical waste at the
same time since they contain mercury . Hence more data would be required to
make out the difference properly.

14. Future works and updates
 The website generated is temporary for the time being, so it has to be hosted on to the
cloud and deployed so that the people can use them.
 Adding some more categories of waste like infectious, radioactive etc.
 Develop an user-friendly graphical user interface for the waste segregation.
 The color bins can be kept together along with the QR code for the website or
application through which the user can find the correct color bin and then dispose it.
 Also this can be integrated with IOT like raspberry Pi which releases an audio
message according to the predictions.
 The color bins can be kept together along the raspberry Pi and a robotic arm which
scans the image and it drops into the respective color bin.
 Also, the collection sites must be increased and pickup for waste from societies
must be done to encourage the activity of household waste segregation.

15. Team profile
 SUBMITTED BY:-
NAME- V. A. Sairam
Dept.- Biomedical Engineering
Year- III Year
e-mail-
sairam.va.2019.bme@Rajalakshmi.edu.in
in
Contact No.- 7010127706
 SUBMITTED BY:-
NAME- M. Madhav
Dept.- Biomedical Engineering
Year- III Year
e-mail-
madhav.m.2019.bme@Rajalakshmi.edu.i
n
Contact No.- 9444752159