this is the ppt for KOA

1. Detection of knee osteoarthritis in X-ray images
using computer vision techniques
Aleena Jamil
Dr. Muhammad Shoaib FA22-RCS-002

2. Agenda
 Introduction
Motivation
Research Objective
 Related Work
Problem Statement
 Methodology
Research Questions
 Evaluation Metrices
 Tentative Timetable
 References

3. Introduction
 knee osteoarthritis (KOA) is the type of osteoarthritis (OA) that has the greatest impact on
people's independence.
 Experienced practitioners frequently employ radiography (X-ray), MRI, CT, ultrasound,
and nuclear medicine.
 The Kellgren-Lawrence (KL) grading system is commonly used to assess the severity of
KOA.

4. Knee Osteoarthritis

5. Stages of knee osteoarthritis
Stage 0 Stage 1 Stage 2 Stage 3 Stage 4

6. Introduction (cont.…)
 Manual processing method is also time-consuming and inefficient.
 Several machine learning techniques have been established in earlier studies to
automatically categorize the KOA severity based on digital information obtained from
radiological images.
 Currently, academics are working on new strategies, such as Deep Learning, to get beyond
these constraints.
 Deep neural networks have been successfully used in several research to automate the KL
grade categorization and forecast the onset of knee osteoarthritis.

7. Motivation
 Knee osteoarthritis (KOA) is a chronic condition that damages the knee cartilage and
makes sufferers less active and in discomfort.
 I have seen many people with knee osteoarthritis. When my supervisor was discussing
various projects in a research meeting, he also mentioned bone fractures. I thought why not
explore this issue so that clinicians can be assisted in early detection.

8. Research Objective

9. Related Work
Author Year Problem Solution Performance Limitations Accuracy
Tiulpin et al
[4]
2020 Develop an
automatic
method to
predict
Kellgren-
Lawrence
(KL) and
Osteoarthritis
CNN Detection of
knee
osteoarthritis
automatically
Lack of diversity
in the datasets
used for training
and testing
82%
Thomas et al
[8]
2020 Classification
and detection
of knee
osteoarthritis
CNN Model's
performance
was evaluated
based on its
ability to predict
KL scores
Small test
set which may
not fully
represent the
diversity of KOA
cases.
70%

10. Related Work (cont..)
Author Year Problem Solution Performance Limitations Accuracy
Antony et al
[12]
2017 Knee joints
detection
and
quantificati
on of KOA
severity
CNN Automatic
detection and
quantification of
KOA severity
Limited test
dataset
86.66%
Tiulpin et al
[13]
2018 Diagnosis of
early knee
osteoarthritis
Deep
Siamese
CNN
Automatic
detection of
knee
osteoarthritis
The Kellgren-
Lawrence (KL)
grading scale
used for knee OA
diagnosis is semi-
quantitative and
suffers from
ambiguity
66.71%

11. Related Work (cont..)
Author Year Problem Solution Performance Limitations Accuracy
Gorriz et al
[14]
2019 Detection of
knee
osteoarthritis
severity
End-to-end
CNN
architecture
Using X-Ray images
quantify the severity
of KOA automatically
Detect knee
osteoarthritis
with limited
dataset
No accuracy
mentioned
in this paper
Wang et al
[27]
2021 Diagnosis of
knee
osteoarthritis
Object
detection
model,
YOLO,
with visual
transformer
Detection of knee
osteoarthritis
Limited test
dataset is used
95.57%

12. Problem Statement
Knee osteoarthritis (KOA) is a chronic condition that damages the knee cartilage and makes
sufferers less active and in discomfort.
Let X= {x1, x2,….xn}
Y = [H, D, MI, MO, S]
Given X and Y, we aim to find a function (f: X -> Y) that maps each knee X-ray image xi ∈ X to one
of the five categories in Y. Formally, our objective is to minimize the classification error:
minf 𝑖=1
𝑛
𝐿 (𝑓 𝑥𝑖 , 𝑦𝑖)

13. Research Questions
The research questions about this study are as follows:
 How can deep learning be used to identify knee osteoarthritis on early stage?
 What effect do various deep learning architectures (such as CNNs and RNNs) have on the
performance of knee osteoarthritis detection?

14. Methodology

15. 1. Data Collection
2. Preprocessing
Augmentation
Normalization
Resizing and Cropping
Segmentation
Data Splitting
3. Model Selection
4. Training and Testing CNN Model
5. Model Evaluation

16. 1. Data Collection
The dataset that is used in this work is available on Kaggle
(https://www.kaggle.com/datasets/gauravduttakiit/osteoarthritis-knee-xray) containing X-ray
images with varying degrees of KOA severity.
2. Data Preprocessing

17. 3. Model Selection
Deep neural network is used for detecting knee OA. The success of convolutional neural
networks (CNNs) in image analysis tasks has led to their widespread application.

18. 4. Training and Testing CNN Model
Training is performed on the training dataset which is the 70% of the whole dataset. Analyze
the final model's performance on the test dataset to give a reliable prediction of how well it
will perform in practice.
5. Model Evaluation
Deep neural network classifies the images into five categories (healthy, doubtful, minimal,
moderate, severe). It is totally depended on the reward.

19. Evaluation Metrices

20. Tentative Timetable
Tasks Sep 2023 0ct 2023 Nov 2023 Dec 23, Jan 24 Feb 2024 Mar 2024
Literature Survey
Problem Identification
Data Collection
Analysis of Results
Thesis Writeup
Paper Writeup

21. References
[4] A. Tiulpin and S. Saarakkala, "Automatic grading of individual knee osteoarthritis
features in plain radiographs using deep convolutional neural networks," Diagnostics, vol. 10,
no. 11, p. 932, 2020.
[8] K. A. Thomas et al., "Automated Classification of Radiographic Knee Osteoarthritis
Severity Using Deep Neural Networks," (in eng), Radiol Artif Intell, vol. 2, no. 2, p. e190065,
Mar 18 2020, doi: 10.1148/ryai.2020190065.
[12] J. Antony, K. McGuinness, K. Moran, and N. E. O’Connor, "Automatic detection of
knee joints and quantification of knee osteoarthritis severity using convolutional neural
networks," in Machine Learning and Data Mining in Pattern Recognition: 13th International
Conference, MLDM 2017, New York, NY, USA, July 15-20, 2017, Proceedings 13, 2017:
Springer, pp. 376-390.
[13] A. Tiulpin, J. Thevenot, E. Rahtu, P. Lehenkari, and S. Saarakkala, "Automatic Knee
Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approach," (in eng),
Sci Rep, vol. 8, no. 1, p. 1727, Jan 29 2018, doi: 10.1038/s41598-018-20132-7.
.

22. [14] M. Górriz, J. Antony, K. McGuinness, X. Giró-i-Nieto, and N. E. O’Connor, "Assessing knee
OA severity with CNN attention-based end-to-end architectures," in International conference on
medical imaging with deep learning, 2019: PMLR, pp. 197-214.
[27] Y. Wang, X. Wang, T. Gao, L. Du, and W. Liu, "An automatic knee osteoarthritis diagnosis
method based on deep learning: data from the osteoarthritis initiative," Journal of Healthcare
Engineering, vol. 2021, pp. 1-10, 2021.