This is the PPT for brain tumor detection using machine learning by using convulational neural network algorithm

1. Brain Tumor Detection Using Machine
Learning
Dissertation
(CSA-CC-4201)
2022-23
Dr. Hari Singh Gour Central University,
Sagar (M.P.)
Department of Computer Science & Application
Presented To:-
Dr.Ranjit Rajak
Guidance by:-
Mr.Satish Chourasiya
Presented by :-
Pooran Raikwar
Y21271029
MCA-4th-sem

2. CONTENTS:-
 TITILE
 INTRODUCTION
 PROBLEM STATEMENT
 OBJECTIVE
 PROPOSED METHODOLOGY
 EXPECTED RESULTS

3. TITLE
Brain Tumor Detection Using
Machine Learning

4. INTRODUCTION
 The brain is a complex organ responsible for
various functions in the human body.
 Brain tumors are abnormal growths of cells
within the brain, and their early detection is
crucial for effective treatment.
 Traditional methods of brain tumor detection
often require invasive procedures, such as
biopsies or CT scans.
 Machine learning techniques offer a non-
invasive and efficient approach to detect brain
tumors.

5. Machine Learning Algorithm
1. Support Vector Machines (SVM)
2. Random Forest
3. Artificial Neural Networks (ANN)
4. Convolutional Neural Networks (CNN)
5. K-Nearest Neighbors (KNN)
6. Naive Bayes
7. Decision Trees
8. Logistic Regression
9. Gradient Boosting Algorithms (e.g., XGBoost, AdaBoost, Gradient
Boosting Machine)
10. Deep Learning models (e.g., U-Net, VGGNet, ResNet)

6. PROBLEM STATEMENT
 The problem lies in accurately and efficiently detecting brain tumors
using machine learning algorithms.
 Existing methods may have limitations in terms of accuracy, speed,
or sensitivity.
 We aim to develop an improved brain tumor detection system using
machine learning.

7. OBJECTIVE
Our objectives are:-
• To develop a machine learning model capable of accurately
detecting brain tumors.
• To create a non-invasive and efficient system that can assist
healthcare professionals in the early detection of brain tumors.
• To improve the speed and accuracy of brain tumor detection
compared to existing methods.

8. PROPOSED METHODOLOGY
Our proposed methodology involves the following steps:
1. Data Collection: Gather a diverse dataset of brain images, including
both tumor and non-tumor samples.
2. Preprocessing: Clean and normalize the collected data to ensure
consistency and remove noise.
3. Feature Extraction: Extract relevant features from the brain images
to represent important tumor characteristics.
4. Model Development: Train a machine learning model using the
extracted features to classify brain images as tumor or non-tumor.
5. Model Evaluation: Assess the performance of the developed model
using evaluation metrics such as accuracy, sensitivity, and specificity.

9. EXPECTED RESULTS
 We expect our developed model to achieve high
accuracy in detecting brain tumors.
 The model should outperform existing methods in terms
of speed and efficiency.
 The system will provide valuable assistance to
healthcare professionals, aiding in early tumor detection
and improving patient outcomes.