this presentation is about a project done by me and my colleague related to computer vision. This project is used to classify the uploaded images of biomedical instruments into prominent ones like ECG, EEG, x-ray machine, CT, MRI, and so on. A website has been developed on which the user can upload any image he is unknown of and the model will tell what instrument it is along with a paragraph explaining the instrument in a crisp manner
In this project, we show that an image can be reconstructed using local descriptors, with or without
complete geometrical metadata in LAB VIEW. We use greedy algorithms to progressively learn the missing
information before reconstruction and colorization is performed .Our experiments show that most of the vital
information about a query image can be recovered even if scale metadata is missing. Compared to images
reconstructed with scale information, we find that there is no significant decline in image quality, and a close
resemblance of the original image post-processing step. Lab VIEW (laboratory Virtual Instrumentation
Engineering Workbench). Lab VIEW is a Graphical Programming Language. It contains icons rather than lines
of text. In contrast to text-based programming languages, where instructions determine program execution, Lab
VIEW uses dataflow programming, where the flow of data determines execution. As the world becomes
increasingly digitalized, it has become intractable to naively search for images merely using pixel information.
As such, researchers have been looking for more efficient methods to perform image matching and retrieval.
Tonsillitis is a disease that can be found in every
part of the world. Moreover, it is one of the main causes
intervening for heart attack and pneumonia. It has been reported
that there are a large number of people having died because of
heart attack and pneumonia. To improve data transfer rates, this
paper proposes Gabor filter design with efficient noise reduction
and less power consumption usage is proposed in this paper.
Using textural properties of anatomical structures the filter
design is suitable for detecting the early stages of disease. The
code for Gabor filter will be developed in MATLAB
In this project, we show that an image can be reconstructed using local descriptors, with or without
complete geometrical metadata in LAB VIEW. We use greedy algorithms to progressively learn the missing
information before reconstruction and colorization is performed .Our experiments show that most of the vital
information about a query image can be recovered even if scale metadata is missing. Compared to images
reconstructed with scale information, we find that there is no significant decline in image quality, and a close
resemblance of the original image post-processing step. Lab VIEW (laboratory Virtual Instrumentation
Engineering Workbench). Lab VIEW is a Graphical Programming Language. It contains icons rather than lines
of text. In contrast to text-based programming languages, where instructions determine program execution, Lab
VIEW uses dataflow programming, where the flow of data determines execution. As the world becomes
increasingly digitalized, it has become intractable to naively search for images merely using pixel information.
As such, researchers have been looking for more efficient methods to perform image matching and retrieval.
Tonsillitis is a disease that can be found in every
part of the world. Moreover, it is one of the main causes
intervening for heart attack and pneumonia. It has been reported
that there are a large number of people having died because of
heart attack and pneumonia. To improve data transfer rates, this
paper proposes Gabor filter design with efficient noise reduction
and less power consumption usage is proposed in this paper.
Using textural properties of anatomical structures the filter
design is suitable for detecting the early stages of disease. The
code for Gabor filter will be developed in MATLAB
Gesture Recognition using Principle Component Analysis & Viola-Jones AlgorithmIJMER
Gesture recognition pertains to recognizing meaningful expressions of motion by a human,
involving the hands, arms, face, head, and/or body. It is of utmost importance in designing an intelligent
and efficient human–computer interface. The applications of gesture recognition are manifold, ranging
from sign language through medical rehabilitation to virtual reality. In this paper, we provide a survey on
gesture recognition with particular emphasis on hand gestures and facial expressions. Applications
involving wavelet transform and principal component analysis for face and hand gesture recognition on
digital images
Nowadays crowd analysis, essential factor about decision management of brand strategy, is not a
controllable field by individuals. Therefore a technology, software products is needed. In this paper we
focused on what we have done about crowd analysis and examination the problem of human detection with
fish-eye lenses cameras. In order to identify human density, one of the machine learning algorithm, which
is Haar Classification algorithm, is used to distinguish human body under different conditions. First,
motion analysis is used to search for meaningful data, and then the desired object is detected by the trained
classifier. Significant data has been sent to the end user via socket programming and human density
analysis is presented.
PCB Faults Detection Using Image Processingijceronline
This paper reviews the digital image processing for PCB fault detection by using MATLAB software. In this project we are implementing different algorithms in sequentional manner with GUI. In this process we are giving two input images one to be inspected for errors i.e. layout of circuit which is implemented on PCB and other one is reference image or standard image of PCB. After these process we can obtained numbers of faults in any respect like hole, Breakout etc. it helps to detect the fault at primary stage of designing. Hence to improve the image quality of compared image we use sharpened process, so we get sharpen images and fault can be detected easily and it is fast and accurate .it reduce the manufacturing cost of PCB
The content based image retrieval (CBIR) technique
is one of the most popular and evolving research areas of the
digital image processing. The goal of CBIR is to extract visual
content like colour, texture or shape, of an image automatically.
This paper proposes an image retrieval method that uses colour
and texture for feature extraction. This system uses the query by
example model. The system allows user to choose the feature on
the basis of which retrieval will take place. For the retrieval
based on colour feature, RGB and HSV models are taken into
consideration. Whereas for texture the GLCM is used for
extracting the textural features which then goes into Vector
Quantization phase to speed up the retrieval process.
An Iot Based Smart Manifold Attendance SystemIJERDJOURNAL
ABSTRACT:- Attendance has been an age old procedure employed in different disciplines of educational institutions. While attendance systems have witnessed growth right from manual techniques to biometrics, plight of taking attendance is undeniable. In fingerprint based attendance monitoring, if fingers get roughed / scratched, it leads to misreading. Also for face recognition, students will have to make a queue and each one will have to wait until their face gets recognised. Our proposed system is employing “manifold attendance” that means employing passive attendance, where at a time, the attendance of multiple people can get captured. We have eliminated the need of queue system / paper-pen system of attendance, and just with a single click the attendance is not only captured, but monitored as well, that too without any human intervention. In the proposed system, creation of database and face detection is done by using the concepts of bounding box, whereas for face recognition we employ histogram equalization and matching technique.
A Flexible Scheme for Transmission Line Fault Identification Using Image Proc...IJEEE
This paper describes a methodology that aims to find and diagnosing faults in transmission lines exploitation image process technique. The image processing techniques have been widely used to solve problem in process of all areas. In this paper, the methodology conjointly uses a digital image process Wavelet Shrinkage function to fault identification and diagnosis. In other words, the purpose is to extract the faulty image from the source with the separation and the co-ordinates of the transmission lines. The segmentation objective is the image division its set of parts and objects, which distinguishes it among others in the scene, are the key to have an improved result in identification of faults.The experimental results indicate that the proposed method provides promising results and is advantageous both in terms of PSNR and in visual quality.
Foreground algorithms for detection and extraction of an object in multimedia...IJECEIAES
Background Subtraction of a foreground object in multimedia is one of the major preprocessing steps involved in many vision-based applications. The main logic for detecting moving objects from the video is difference of the current frame and a reference frame which is called “background image” and this method is known as frame differencing method. Background Subtraction is widely used for real-time motion gesture recognition to be used in gesture enabled items like vehicles or automated gadgets. It is also used in content-based video coding, traffic monitoring, object tracking, digital forensics and human-computer interaction. Now-a-days due to advent in technology it is noticed that most of the conferences, meetings and interviews are done on video calls. It’s quite obvious that a conference room like atmosphere is not always readily available at any point of time. To eradicate this issue, an efficient algorithm for foreground extraction in a multimedia on video calls is very much needed. This paper is not to just build Background Subtraction application for Mobile Platform but to optimize the existing OpenCV algorithm to work on limited resources on mobile platform without reducing the performance. In this paper, comparison of various foreground detection, extraction and feature detection algorithms are done on mobile platform using OpenCV. The set of experiments were conducted to appraise the efficiency of each algorithm over the other. The overall performances of these algorithms were compared on the basis of execution time, resolution and resources required.
Detection and tracking of red color by using matlabAbhiraj Bohra
This program just tracks all red color objects and draws a bounding box around them. This works on the difference between frames concept. Every frame in the video is returned as an rgb image on which we can do image processing.
Ieee projects 2012 2013 - Digital Image ProcessingK Sundaresh Ka
ieee projects download, base paper for ieee projects, ieee projects list, ieee projects titles, ieee projects for cse, ieee projects on networking,ieee projects 2012, ieee projects 2013, final year project, computer science final year projects, final year projects for information technology, ieee final year projects, final year students projects, students projects in java, students projects download, students projects in java with source code, students projects architecture, free ieee papers
Detection of medical instruments project- PART 2Sairam Adithya
this presentation is a continuation of the previous one. In this presentation, the work process for individual steps has been clearly explained with snippets of code taken from the source code. This is present along with output visualization, advantages and conclusion.
Gesture Recognition using Principle Component Analysis & Viola-Jones AlgorithmIJMER
Gesture recognition pertains to recognizing meaningful expressions of motion by a human,
involving the hands, arms, face, head, and/or body. It is of utmost importance in designing an intelligent
and efficient human–computer interface. The applications of gesture recognition are manifold, ranging
from sign language through medical rehabilitation to virtual reality. In this paper, we provide a survey on
gesture recognition with particular emphasis on hand gestures and facial expressions. Applications
involving wavelet transform and principal component analysis for face and hand gesture recognition on
digital images
Nowadays crowd analysis, essential factor about decision management of brand strategy, is not a
controllable field by individuals. Therefore a technology, software products is needed. In this paper we
focused on what we have done about crowd analysis and examination the problem of human detection with
fish-eye lenses cameras. In order to identify human density, one of the machine learning algorithm, which
is Haar Classification algorithm, is used to distinguish human body under different conditions. First,
motion analysis is used to search for meaningful data, and then the desired object is detected by the trained
classifier. Significant data has been sent to the end user via socket programming and human density
analysis is presented.
PCB Faults Detection Using Image Processingijceronline
This paper reviews the digital image processing for PCB fault detection by using MATLAB software. In this project we are implementing different algorithms in sequentional manner with GUI. In this process we are giving two input images one to be inspected for errors i.e. layout of circuit which is implemented on PCB and other one is reference image or standard image of PCB. After these process we can obtained numbers of faults in any respect like hole, Breakout etc. it helps to detect the fault at primary stage of designing. Hence to improve the image quality of compared image we use sharpened process, so we get sharpen images and fault can be detected easily and it is fast and accurate .it reduce the manufacturing cost of PCB
The content based image retrieval (CBIR) technique
is one of the most popular and evolving research areas of the
digital image processing. The goal of CBIR is to extract visual
content like colour, texture or shape, of an image automatically.
This paper proposes an image retrieval method that uses colour
and texture for feature extraction. This system uses the query by
example model. The system allows user to choose the feature on
the basis of which retrieval will take place. For the retrieval
based on colour feature, RGB and HSV models are taken into
consideration. Whereas for texture the GLCM is used for
extracting the textural features which then goes into Vector
Quantization phase to speed up the retrieval process.
An Iot Based Smart Manifold Attendance SystemIJERDJOURNAL
ABSTRACT:- Attendance has been an age old procedure employed in different disciplines of educational institutions. While attendance systems have witnessed growth right from manual techniques to biometrics, plight of taking attendance is undeniable. In fingerprint based attendance monitoring, if fingers get roughed / scratched, it leads to misreading. Also for face recognition, students will have to make a queue and each one will have to wait until their face gets recognised. Our proposed system is employing “manifold attendance” that means employing passive attendance, where at a time, the attendance of multiple people can get captured. We have eliminated the need of queue system / paper-pen system of attendance, and just with a single click the attendance is not only captured, but monitored as well, that too without any human intervention. In the proposed system, creation of database and face detection is done by using the concepts of bounding box, whereas for face recognition we employ histogram equalization and matching technique.
A Flexible Scheme for Transmission Line Fault Identification Using Image Proc...IJEEE
This paper describes a methodology that aims to find and diagnosing faults in transmission lines exploitation image process technique. The image processing techniques have been widely used to solve problem in process of all areas. In this paper, the methodology conjointly uses a digital image process Wavelet Shrinkage function to fault identification and diagnosis. In other words, the purpose is to extract the faulty image from the source with the separation and the co-ordinates of the transmission lines. The segmentation objective is the image division its set of parts and objects, which distinguishes it among others in the scene, are the key to have an improved result in identification of faults.The experimental results indicate that the proposed method provides promising results and is advantageous both in terms of PSNR and in visual quality.
Foreground algorithms for detection and extraction of an object in multimedia...IJECEIAES
Background Subtraction of a foreground object in multimedia is one of the major preprocessing steps involved in many vision-based applications. The main logic for detecting moving objects from the video is difference of the current frame and a reference frame which is called “background image” and this method is known as frame differencing method. Background Subtraction is widely used for real-time motion gesture recognition to be used in gesture enabled items like vehicles or automated gadgets. It is also used in content-based video coding, traffic monitoring, object tracking, digital forensics and human-computer interaction. Now-a-days due to advent in technology it is noticed that most of the conferences, meetings and interviews are done on video calls. It’s quite obvious that a conference room like atmosphere is not always readily available at any point of time. To eradicate this issue, an efficient algorithm for foreground extraction in a multimedia on video calls is very much needed. This paper is not to just build Background Subtraction application for Mobile Platform but to optimize the existing OpenCV algorithm to work on limited resources on mobile platform without reducing the performance. In this paper, comparison of various foreground detection, extraction and feature detection algorithms are done on mobile platform using OpenCV. The set of experiments were conducted to appraise the efficiency of each algorithm over the other. The overall performances of these algorithms were compared on the basis of execution time, resolution and resources required.
Detection and tracking of red color by using matlabAbhiraj Bohra
This program just tracks all red color objects and draws a bounding box around them. This works on the difference between frames concept. Every frame in the video is returned as an rgb image on which we can do image processing.
Ieee projects 2012 2013 - Digital Image ProcessingK Sundaresh Ka
ieee projects download, base paper for ieee projects, ieee projects list, ieee projects titles, ieee projects for cse, ieee projects on networking,ieee projects 2012, ieee projects 2013, final year project, computer science final year projects, final year projects for information technology, ieee final year projects, final year students projects, students projects in java, students projects download, students projects in java with source code, students projects architecture, free ieee papers
Detection of medical instruments project- PART 2Sairam Adithya
this presentation is a continuation of the previous one. In this presentation, the work process for individual steps has been clearly explained with snippets of code taken from the source code. This is present along with output visualization, advantages and conclusion.
leewayhertz.com-HOW IS A VISION TRANSFORMER MODEL ViT BUILT AND IMPLEMENTED.pdfrobertsamuel23
Recent years have seen deep learning completely transform computer vision and image
processing. Convolutional neural networks (CNNs) have been the driving force behind
this transformation due to their ability to efficiently process large amounts of data,
enabling the extraction of even the smallest image features.
In the healthcare industry, patients are often exposed to harmful pathogens due to a lack of compliance to hand hygiene protocol. The vast majority of healthcare professionals do not abide by hand hygiene standards established by the World Health Organization, facilitating the spread of nosocomial (hospital-acquired) infections. When representatives trained in proper handwashing procedures monitored medical professionals, there was a significant increase in compliance with proper protocol. Given this correlation between observance and adherence, a Hygiene Monitoring System was developed to monitor handwashing through the application of machine learning. The embedded system captured, processed, and compared instances of handwashing to the proper procedure. An implementation of this system would encourage healthcare professionals to follow the official protocol denoted by the World Health Organization and dramatically reduce the likelihood of healthcare–associated infections.
CNN FEATURES ARE ALSO GREAT AT UNSUPERVISED CLASSIFICATION cscpconf
This paper aims at providing insight on the transferability of deep CNN features to
unsupervised problems. We study the impact of different pretrained CNN feature extractors on
the problem of image set clustering for object classification as well as fine-grained
classification. We propose a rather straightforward pipeline combining deep-feature extraction
using a CNN pretrained on ImageNet and a classic clustering algorithm to classify sets of
images. This approach is compared to state-of-the-art algorithms in image-clustering and
provides better results. These results strengthen the belief that supervised training of deep CNN
on large datasets, with a large variability of classes, extracts better features than most carefully
designed engineering approaches, even for unsupervised tasks. We also validate our approach
on a robotic application, consisting in sorting and storing objects smartly based on clustering
This presentation is all about counters, focusing on synchronous and asynchronous counters. The unique feature is the incorporation of the circuit images generated from MULTISIM software imparting practical knowledge to the users.
SEQUENTIAL LOGIC CIRCUITS (FLIP FLOPS AND LATCHES)Sairam Adithya
this presentation is about the sequential logic circuits, mainly concentrating on flip-flops and latches. a unique feature in this presentation is the incorporation of circuit images generated from Multisim software imparting practical knowledge to the users. this consists of both the active low and high versions of different circuits.
Medical waste segregation using deep learningSairam Adithya
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.
this is the last presentation in the OpenCV series. this presentation is about the inculcation of different shapes into the given image. It also includes automated shapes using haarcascades. tasks like face detection, face blocking, eye detection, eye blocking, smile detection, smile blocking and so on are displayed in this presentation. the code along with the output images are displayed in the presentation. Hope this presentation helps!!!.
Continuing the presentation series, the fourth part is about the blurring and sharpening of images. the manual method of doing the operations is given along with some functions for blurring. the next is about edge detection algorithms like Canny, Sobel, and Prewitt. also, the dilates and the eroded images are provided along with the canny ones.
I HAVE WORKED HARD FOR THIS PRESENTATION!! SO PLEASE SUPPORT GUYS!!!
this is the third part in the opencv presentation series. this presentation is about some of the basic operations on images like flipping, cropping, rotating, resizing and extracting B, G & R equivalents from a colour image. the code along with the output images are also added to the explanation for better understanding.
this presentation is about colormaps. the definition of colormap with the syntax for the function of applying colormaps is provided. the names for the 22 standard colormaps along with their indices are also provided. the code and output image for each of the colormap are also provided.
This is the first part of the presentation series on one of the powerful open sources libraries, the opencv. this presentation is about the introduction, installation, some basic functions on images and some basic image processing on the images
This presentation is about the introduction to Diabetes Mellitus. This lifestyle disease has become common in the current generation. This presentation is about diabetes, its classification, the definition of DM, individual types with causes, events, changes, symptoms and treatments.
TASK-OPTIMIZED DEEP NEURAL NETWORK TO REPLICATE THE HUMAN AUDITORY CORTEXSairam Adithya
this presentation is about a research paper which deals with the development of a deep-learning model to replicate the human auditor system. A lot of interesting facts about the human auditory cortex has been found out through the model. Ultimately, the model is able to replicate the human both task-wise and structure-wise. In other words, appropriate information about the brain was obtained through the model which was performing like the human.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
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2. AI is one of the fastest growing data driven technology that plays a futuristic role in
healthcare industry. Several ML, Image processing and DL algorithms have taken a vital role
in performing clinical diagnoses and suggesting treatments. By 2021, the artificial
intelligence (AI) market in healthcare industry is expected to grow by $6.6 billion.
This project involves a system that helps in detecting various biomedical devices or
instruments via digital image processing technique. It consists of:
Datasets (Images) obtained from various sources.
A large number of output classes.
Various types of categories, parts or components related to biomedical instruments.
Various types of scans that help in providing a lot of information.
Simple interface to interact with various inputs and get required output information.
This reflects a basic implementation of AI in healthcare industry and associated sectors in
order to classify various images under several categories of medical devices.
3. OBJECTIVES
The prime objective is to identify and classify the given image among the 20 most
popular medical devices and provide a brief overview on the detected instrument
To develop a dataset containing various images of biomedical devices or instruments.
Trusted sources are used to get practical images of various instruments.
To develop an algorithm for digital image processing. This can include any pre-trained
neural network.
To Test, train and evaluate the performance of the algorithm containing a lot of features.
To Utilize this algorithm for classifying various images of biomedical instruments.
To Form of website, portal or an app by which the input images are uploaded and
processed to get the required output.
Along with the output some information is provided to develop a basic understanding of
the device.
4. 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 other operations on images. The primary
function of CNN’s is to extract features from images basically they convert multi-dimensional
images to 1 dimensional vectors. These CNN’s are combined with Fully Connected layers to
process the vector.
VGG-16 is one such deep CNN which was developed by the Oxford University. In this project this
model has been used for the detection of medical devices.
The training dataset includes about 40 images per class and the source of these images are
shopping websites like alibaba.com and indiamart.com etc.
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 ngrok was used to host a website and create URL for the web application.
5. WORKING CONCEPT
• The convolutional neural network mainly works on image data. It is used for feature
extraction from the image. This is a partially connected neural network. Image can be
interpreted by us but not by machines. Hence they interpret images as a vector whose
values represent the colour intensity of the image. Every colour can be expressed as a
vector of 3-D known as RGB- Red Green Blue. The size of the vector is equal to the
dimensions of image.
• Convolution in mathematics refers to the process of combining two different functions. With
respect to CNN, convolution occurs between the image and the filter or kernel. Convolution
itself is one of the processes done on the image. Here also the operation is mathematical. It
is a kind of operation on two vectors. The input image gets converted into vector based on
colour and dimension. The kernel or filter is a predefined vector with fixed values to perform
various functions onto the image.
6. We have seen that there is a reduction of dimension in the output vector. A technique
known as padding is done to preserve the original dimensions in the output vector. The only
change in this process is that we add a boundary of ‘0s’ over the input vector and then do
the convolution process.
It is not necessary that the filter or kernel must be applied to all the cells. The pattern of
applying the kernel onto the input vector is determined using the stride. It determines the
shift or gaps in the cells where the filter has to be applied.
This is another aspect of the CNN. There are different types of pooling like min pooling, max
pooling, avg pooling etc. the process is same as before i.e. the kernel vector slides over the
input vector and does computations on the dot product. If a 3*3 kernels is considered then
it is applied over a 3*3 region inside the vector, it finds the dot product in the case of
convolution. The same in pooling finds a particular value and substitutes that value in the
output vector. The kernel value decides the type of pooling.
The convolution and pooling are the basis for feature extraction. The vector obtained from
this step is fed into a FFN which then does the required task on the image.
the FFN consists of neurons connected to each other. The last layer of FFN consists of
neurons equal to the number of output classes. (in this case 20)
7. This project involves VGG-16 network. This Neural network consists of 1 layers in total. There
are 13 layers pertaining to feature extraction and 3 layers pertaining to classification.
The output dimension is changed into 1*1*20 and the given images must be reshaped to
224*224 since this dimension is compatible for VGGNet. The below table shows the total
number of parameters. About 120 Million parameters come from the FC layers and 16M
parameters from CNN Layer Value of parameters
Convolution 16M
FF1 102M
FF2 16M
Total 134M
8. It takes days to train about 132 Million parameters. Hence the GPU (Graphics Processing
Unit) is used to accelerate the training time to hours.
To achieve even faster training time pre-trained CNNs’ are used. The researchers have
already trained the VGGNet and stored them in the keras library. As a result the training time
significantly drops to minutes but there will be less accuracy. To get the best accuracy, the
network has to be trained which can take hours.
the streamlit is one such library in python which is used to create and design web
application out of the code. The design for the website is done here which includes a dialog
box for uploading images and submit button which when pressed the output is given. The
instance when the application is opened the training of the model has to run in the
background and when the submit is given the evaluation occurs.
The ngrok is used to host the website by providing an URL. There are some problems in the
website which could be overcome by using the paid version.
The URL is temporary and does not exist long. The URL cannot withstand large traffic (no of
users at a time) and the session can expire on reloading. It is recommended to upgrade to a
premium version to overcome these problems.
9. 1. Uploading the dataset. The image dataset was available in the google drive. So we
had to mount google drive into google colab.
2. Providing the path for the training and testing image datsets.
3. Reshaping the images to (224,224) the size which is appropriate for VGGNet.
4. Importing VGGNet 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 20 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.
8. Using the ImageDataGenerator to obtain the samples for training and testing.
9. Training the model for the desired number of steps and epochs.
10. 11. Importing streamlit and required libraries.
12. Creating the design for the website.
13. Importing the stable ngrok zip file.
14. Unzipping the zip file.
15. Importing the https from ngrok
16. Combining the https from the obtained URL.
17. Running the application on the website.
13. A website, portal or an app could be developed for better implementation of the
trained algorithm.
Various images can be labelled in their respective category of medical devices (in a
quick manner).
This model can be used for educative purposes. It can be used for students in an IV
to hospital/industry to learn on the biomedical devices.
It can be used by common man to know some facts and basic working of the
common biomedical devices.
On an advance level, the algorithm could be deployed in industries or PCBs with a
mounted camera to classify the real biomedical instrument.
Upon training with medical datasets, this could help in categorizing images (or scans)
related to a particular organ or organ system.
Upon training with medication datasets, this could help in a better drug classification
via image analysis technique.
14. ADVANTAGES
People could use this system (as website or an app) to gain information about several
medical devices surrounding them.
In a short period of time, the required output information is obtained from the
corresponding image input.
Several scans can be classified into various categories depending upon the recording
instrument or technique.
Better accuracy has been obtained (about 93%) on the datasets obtained from online
shopping websites. Can achieve even better with images from hospitals.
Several platforms or sites can use digital image processing as a feature to label various
medical instruments.
This idea could help the healthcare industry by keep a timeline or track of
advancements in several biomedical instruments, devices and techniques.
15. DISADVANTAGES
Without proper and practical datasets, the prediction or classification system
cannot detect with high accuracy.
The design for the ECG, EEG and EMG machines of both printer version and video
version are mostly similar and the difference is in the point of application. Hence
there is some lack of clarity in detecting these instruments
The design for the MRI and CT scan machines are similar and also most of the CT
scan machines perform PET scan. There is some misconception arised due to the
above points.
Thee is some confusion in detecting pulse oximeter an blood pressure machine
due to the similarities in the display monitor.
If most of the dataset is trained upon low-end or older version of medical devices
or instruments (without updating to the portable and advance ones), then this will
cause a problem in detecting advance and higher-end devices.
16. The dataset does not house enough samples to get the ideal prediction. The model
has to be trained with a lot of images.
We have taken images from shopping websites. It is recommended to take pictures
of devices straight from the hospital/manufacturing industry. We can take images
from different angles, positions, views and distances. These images can improve
the model’s function.
Taking images straight from the hospital or manufacturing industry is practically
not possible in this pandemic situation.
The URL obtained from the ngrok is not permanent and has some issues since we
are using the free version. Also there are some errors while uploading the image
which could not be rectified and the website stops working when reloaded.
17. With respect to the futuristic developments in image processing technology, it is expected to
gain market growth from 2020 to 2027. It is expected to reach USD 25,702 million by 2027
(growth rate of 21.8%). Healthcare is one of the prime industries which requires AI and Image
processing techniques to provide better classification and diagnostic results. Latest data shows
that:
Increase in the manufacturing of ventilators to treat COVID-19 patients.
Increased production of pulse oximeters and oxygen concentrators with reference to COVID.
Several startups have used their platforms to promote portable and advance versions of
medical devices. (AgVa healthcare)
Detection, scanning and testing techniques are modified to cope up with the increasing risk
of chronic diseases.
Incorporation of deep learning methods in particular CNN’s for the detection of diseases and
more applications in the medical fields.
18. CONCLUSION
Hence we have successfully implemented VGGNet to detect and classify the given image
among the 20 common biomedical instruments and in this process we have achieved about
93% accuracy. The model was able to correctly detect the uploaded images of biomedical
instruments of the 20 classes. The model is also able to detect the machine even if the image
of the parts are given (model correctly predicted endoscopy when image of the camera was
uploaded and catheter when the image of the tube was uploaded) .
Accuracy: 93.4%
Time Taken: 5 mins and 12 secs (depend on the epochs)
19. PROFILE OF THE TEAM
SUBMITTED BY:-
NAME- Arjun Bhattacharya
Dept.- Biomedical Engineering
Year- II Year
e-mail-
arjun.bhattacharya.2019.bme@Raj
alakshmi.edu.in
Contact No.- 7091832747
SUBMITTED BY:-
NAME- V. A. Sairam
Dept.- Biomedical Engineering
Year- II Year
e-mail-
sairam.va.2019.bme@Rajalakshmi.
edu.in
Contact No.- 7010127706
COLLEGE→ Rajalakshmi Engineering