Presentation PFE

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Mohammed Boussardi

Présentation de mon projet PFE avec mes collègues GASSIR Chaimae, AIT BENAISSA Mohamed-hamza, et ESSADEQ Ayoub

Engineering

LUNG CANCER DETECTION
1
CADI AYYAD UNIVERSITY
HIGH SCHOOL OF TECHNOLOGY SAFI
COMPUTER SCIENCE
Realized by:
Mohamed -Hamza Ait Benaissa
Mohamed Boussardi
Chaimae Gassir
Ayoub Essadeq
Supervised by:
Mr. Alaoui Fdili Othmane
Tutored by:
Gamal Mohamed

Plan
2
1
4
5
3
2
Introduction
The application
Theorical Concepts
Models
Conclusion

Introduction
According to a recent study, Doctors spend 75% of
each patient visit on consulting electronic medical records, and
they may made errors predicting them that’s why we include AI
in medical filed because it help to increase the levels of
accuracy in detecting and diagnosing disease.
In this presentation , we will present an application
that can detect lung cancer by selecting an X-ray or
histopathological image.
3

Theorical Concepts : object detection
5
- What is Object detection
?
- How we can achieve this
operation ?

Theorical Concepts : CNN
6
• CNN is a deep learning
algorithm.
• Convolutional neural
networks are generally
composed of the following
layers:
Convolution
Max-pooling
Fully-connected

Theorical Concepts : input
7
 How computer see an
image?
• Machine sees the image
as a table of numbers
between 0 and 255.
• The numbers are called
pixels .

Theorical Concepts : convolution
8
• Extract the high-level
features such as edges
from the input image.
• Image multiplicate by a
filter.

Theorical Concepts : max pooling
9
• Uses the maximum
value from a cluster
of neurons at the
prior layer.
• Reduces the
dimensions of the
image.

Theorical Concepts : machine learning
10
• Supervised learning: train the
algorithm using data which is well
“labeled”.
• Unsupervised learning: analyze
and cluster unlabeled data.

Models : project I
12
Data
• This Project is using a Kaggle dataset That
contains five classes :
- colon benign
- colon malignant
- lung benign
-lung malignant ACA
-lung malignant SCC
Data & Tools
Tools
• Pytorch
• Google colab
• spyder

Models : project I
13
1. Image Preprocessing
2. Histopathological lung vs
Others
3. Lung benign vs malignant
classification
4. Please insert a
Histopathological lung image
5. Lung malignant type
classification
Architecture

Models : project I
14
CNN 1
- 2 convolutional layers (Kernel 5x5)
- 2 Max Pool (Kernel 4x4)
- 3 convolutional layers (Kernel 4x4)
- 1 convolutional layers (Kernel 5x5)
- 1 Max Pool (Kernel 4x4)
CNN 3
CNN 2
- 2 convolutional layers (Kernel 5x5)
- 2 Max Pool (Kernel 2x2)
CNNs

Models : project I
15
Training
Loss
Validation
Loss
Training
Error
Validation
Error
Execution
time(s)
CNN 1: Lung
vs. Colon
0.043 0.074 0.017 0.024 1317.23
CNN 2: Lung
Benign vs.
Malignant
0.024 0.027 0.010 0.012 3532.80
CNN 3: Lung
SCC vs. ACA
0.098 0.355 0.033 0.106 3941.86
Training

Models : project II
17
VGG 16 Architecture
• Architecture
• Modification
• Tools : keras & tensrflow
• Dataset : kaggle
• 2 categories : normal / peneumonia

- Load the training model.
- Take an image from the testing set.
18
Models : project II
Testing

conclusion
21
We have learned a lot of things in this project:
• How dangerous lung cancer is.
• New technologies and domain.
• How to work in groups.
• Any problem has a solution.
But generally, we achieved our goals and we developed our application
where detect lung cancer and returns the results.

22
Any questions?
Thanks for your
Attention :)

This document is a final report on lung cancer detection using deep learning. It provides theoretical concepts on artificial intelligence, machine learning, deep learning, computer vision, and neural networks. It discusses key machine learning algorithms like supervised and unsupervised learning. Deep learning concepts covered include neural networks, perceptrons, quantifying loss, and examples of deep learning algorithms. The goal of the report is to realize an approach for lung cancer detection using these artificial intelligence and computer vision techniques.

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原版一模一样【微信：741003700 】【(uofo毕业证书)美国俄勒冈大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。本公司拥有海外各大学样板无数，能完美还原。 1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700 【主营项目】一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！二.真实使馆公证(即留学回国人员证明,不成功不收费) 三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度) 如果您处于以下几种情况： ◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】 ◇面对父母的压力，希望尽快拿到； ◇不清楚认证流程以及材料该如何准备； ◇回国时间很长，忘记办理； ◇回国马上就要找工作，办给用人单位看； ◇企事业单位必须要求办理的 ◇需要报考公务员、购买免税车、落转户口 ◇申请留学生创业基金留信网认证的作用: 1:该专业认证可证明留学生真实身份 2:同时对留学生所学专业登记给予评定 3:国家专业人才认证中心颁发入库证书 4:这个认证书并且可以归档倒地方 5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息 6:个人职称评审加20分 7:个人信誉贷款加10分 8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才办理(uofo毕业证书)美国俄勒冈大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。办理(uofo毕业证书)美国俄勒冈大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：校徽：象征着学校的荣誉和传承。校名:学校英文全称授予学位：本部分将注明获得的具体学位名称。毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。办理(uofo毕业证书)美国俄勒冈大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。综上所述，办理(uofo毕业证书)美国俄勒冈大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。

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Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network

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一比一原版(uofo毕业证书)美国俄勒冈大学毕业证如何办理

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Presentation PFE

1. LUNG CANCER DETECTION 1 CADI AYYAD UNIVERSITY HIGH SCHOOL OF TECHNOLOGY SAFI COMPUTER SCIENCE Realized by: Mohamed -Hamza Ait Benaissa Mohamed Boussardi Chaimae Gassir Ayoub Essadeq Supervised by: Mr. Alaoui Fdili Othmane Tutored by: Gamal Mohamed

2. Plan 2 1 4 5 3 2 Introduction The application Theorical Concepts Models Conclusion

3. Introduction According to a recent study, Doctors spend 75% of each patient visit on consulting electronic medical records, and they may made errors predicting them that’s why we include AI in medical filed because it help to increase the levels of accuracy in detecting and diagnosing disease. In this presentation , we will present an application that can detect lung cancer by selecting an X-ray or histopathological image. 3

4. Theorical Concepts

5. Theorical Concepts : object detection 5 - What is Object detection ? - How we can achieve this operation ?

6. Theorical Concepts : CNN 6 • CNN is a deep learning algorithm. • Convolutional neural networks are generally composed of the following layers: Convolution Max-pooling Fully-connected

7. Theorical Concepts : input 7  How computer see an image? • Machine sees the image as a table of numbers between 0 and 255. • The numbers are called pixels .

8. Theorical Concepts : convolution 8 • Extract the high-level features such as edges from the input image. • Image multiplicate by a filter.

9. Theorical Concepts : max pooling 9 • Uses the maximum value from a cluster of neurons at the prior layer. • Reduces the dimensions of the image.

10. Theorical Concepts : machine learning 10 • Supervised learning: train the algorithm using data which is well “labeled”. • Unsupervised learning: analyze and cluster unlabeled data.

11. Models

12. Models : project I 12 Data • This Project is using a Kaggle dataset That contains five classes : - colon benign - colon malignant - lung benign -lung malignant ACA -lung malignant SCC Data & Tools Tools • Pytorch • Google colab • spyder

13. Models : project I 13 1. Image Preprocessing 2. Histopathological lung vs Others 3. Lung benign vs malignant classification 4. Please insert a Histopathological lung image 5. Lung malignant type classification Architecture

14. Models : project I 14 CNN 1 - 2 convolutional layers (Kernel 5x5) - 2 Max Pool (Kernel 4x4) - 3 convolutional layers (Kernel 4x4) - 1 convolutional layers (Kernel 5x5) - 1 Max Pool (Kernel 4x4) CNN 3 CNN 2 - 2 convolutional layers (Kernel 5x5) - 2 Max Pool (Kernel 2x2) CNNs

15. Models : project I 15 Training Loss Validation Loss Training Error Validation Error Execution time(s) CNN 1: Lung vs. Colon 0.043 0.074 0.017 0.024 1317.23 CNN 2: Lung Benign vs. Malignant 0.024 0.027 0.010 0.012 3532.80 CNN 3: Lung SCC vs. ACA 0.098 0.355 0.033 0.106 3941.86 Training

16. Models Project II

17. Models : project II 17 VGG 16 Architecture • Architecture • Modification • Tools : keras & tensrflow • Dataset : kaggle • 2 categories : normal / peneumonia

18. - Load the training model. - Take an image from the testing set. 18 Models : project II Testing

19. Models : project II 19 Output

20. Application

21. conclusion 21 We have learned a lot of things in this project: • How dangerous lung cancer is. • New technologies and domain. • How to work in groups. • Any problem has a solution. But generally, we achieved our goals and we developed our application where detect lung cancer and returns the results.

22. 22 Any questions? Thanks for your Attention :)

Presentation PFE

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