Merge pull request #476 from ARPIT2128/main

README Enhancement - Brain Tumor Detection

Brain Tumor Detection/Dataset/README.md

@@ -1,6 +1,28 @@
-The link for the Brain Tumor Dataset - https://www.kaggle.com/datasets/ahmedhamada0/brain-tumor-detection . 
+# 🧠 Br35H :: Brain Tumor Detection 2020
 
-It has a total of 3060 Brain MRI images . 
-- Non-Tumorous Images - 1500
-- Tumorous Images - 1500 
-- Additional 60 images in "pred" folder . 
+![Brain Tumor Detection](converpic.jpg)
+
+## 📝 Abstract
+
+A brain tumor is a formidable disease affecting both children and adults, constituting 85 to 90 percent of all primary Central Nervous System (CNS) tumors. Annually, around 11,700 people receive a brain tumor diagnosis, with a 5-year survival rate of approximately 34 percent for men and 36 percent for women. Proper treatment, planning, and accurate diagnostics are crucial to improving patient life expectancy.
+
+The dataset focuses on automated classification techniques using Machine Learning (ML) and Artificial Intelligence (AI), specifically employing Deep Learning Algorithms such as Convolutional Neural Network (CNN), Artificial Neural Network (ANN), and Transfer Learning (TL). These techniques offer higher accuracy than manual classification, aiding doctors worldwide in efficient detection and classification.
+
+## 🌐 Context
+
+Brain tumors present complexities in size and location, requiring expertise for accurate analysis. Developing countries often face challenges due to a shortage of skilled doctors and insufficient knowledge about tumors. An automated system on the cloud can address these issues, providing a faster and more accessible solution.
+
+## 🔍 Definition
+
+To detect and classify brain tumors using CNN and TL, leveraging Deep Learning as an asset, and examining tumor positions through segmentation.
+
+## ℹ️ About the Data
+
+The dataset comprises three folders:
+- **Yes:** Contains 1500 Brain MRI Images with tumorous conditions.
+- **No:** Consists of 1500 Brain MRI Images without tumors.
+- **Pred:** Includes an additional 60 images for prediction.
+
+## 🙌 Acknowledgments
+
+### 🥸**AUTHOR NAME:** Ahmed Hamada

Brain Tumor Detection/Model/README.md

@@ -1,56 +1,113 @@
-# BRAIN TUMOR DETECTION
-## GOAL
+# BRAIN TUMOR DETECTION 🧠💻
+## 🎯 Goal
+
+The primary objective of this project is to develop Convolution models capable of determining whether an individual has a brain tumor or not. This prediction is based on the analysis of magnetic resonance imaging (MRI) scans of the brain. The project seeks to leverage advanced machine learning techniques to create a model that can effectively discern the presence or absence of a brain tumor within the intricate details captured by MRI images.
+
+## 🧵 Dataset
+
+Dataset Link: [Brain Tumor Detection Dataset](https://www.kaggle.com/datasets/ahmedhamada0/brain-tumor-detection)
+
+The dataset comprises approximately 3060 MRI images of the brain, categorized into:
+- Non-Tumorous Images: 1500
+- Tumorous Images: 1500
+- Additional 60 images in the "pred" folder.
 
-To predict whether a person has brain tumor or not based on available MRI images of Brain . 
+## 🧾 Description
 
-## DATASET
+This project aims to develop a model capable of accurately detecting the presence of a brain tumor in MRI images. The dataset is used for training and testing the models.
 
-https://www.kaggle.com/datasets/ahmedhamada0/brain-tumor-detection
+## 🧮 What I had done!
 
-The dataset contains around 3060 MRI images of brain .
-- Non-Tumorous Images - 1500
-- Tumorous Images - 1500 
-- Additional 60 images in "pred" folder . 
+1. **Data Preprocessing:**
+   - Load and explore the dataset.
+   - Handle missing data and outliers.
+   - Normalize and resize images.
 
-## SOME OF THE TUMOROUS BRAIN MRI IMAGES
-<img src = "https://user-images.githubusercontent.com/72621930/219711653-f89efe73-84ae-4b76-aae3-4f175776b767.jpg" width = "200"> <img src = "https://user-images.githubusercontent.com/72621930/219716592-507e25fc-3aa3-40eb-8a88-73ec99d56914.jpg" width = "200">
-![y14](https://user-images.githubusercontent.com/72621930/219711854-6e64f8c8-1a43-469f-ac7e-57cff0c5ce4a.jpg)
-<img src = "https://user-images.githubusercontent.com/72621930/219712189-12c1172f-5c4f-40b0-9e13-f7e709e9850c.jpg" width = "200"> <img src = "https://user-images.githubusercontent.com/72621930/219712231-6a43351d-747a-4cca-9755-0261cda9a834.jpg" width = "180">
-<img src = "https://user-images.githubusercontent.com/72621930/219720376-70ecb711-a8c8-496b-be3b-f1f5b3d00563.jpg" width = "207">
-<img src = "https://user-images.githubusercontent.com/72621930/219720379-e72a9146-04e1-41bb-acf3-e82e347019ab.jpg" width = "196">
-<img src = "https://user-images.githubusercontent.com/72621930/219720393-1e9ea66e-dbce-4d70-a04e-5b4ee1bbec9f.jpg"  width = "197">
-<img src = "https://user-images.githubusercontent.com/72621930/219720422-a400acf6-1ce4-47c5-81d2-faabfed37317.jpg" width = "197">
-<img src = "https://user-images.githubusercontent.com/72621930/219720445-97515e55-9974-4435-940a-8725e04f6390.jpg" width = "197">
+2. **Train-Test Split:**
+   - Split the dataset into training (80%) and testing (20%) sets.
 
+3. **Model Training:**
+   - Implement CNN (Convolutional Neural Network).
+   - Utilize pre-trained models: VGG16 and RESNET 50.
+   - Fine-tune models for brain tumor detection.
 
+4. **Evaluation:**
+   - Assess model performance on the test set.
+   - Analyze and interpret the results.
 
-## SOME OF THE NON-TUMOROUS BRAIN MRI IMAGES
-<img src = "https://user-images.githubusercontent.com/72621930/219717582-a8399573-1365-41b9-881d-34962845d91f.jpg" width = "199"><img src ="https://user-images.githubusercontent.com/72621930/219717613-839ba23c-4cbe-4273-95a9-78e1205384ff.jpg" width = "205">
-<img src = "https://user-images.githubusercontent.com/72621930/219717624-9b7aeb0b-4c00-44e4-afe6-0c2f5c8500ac.jpg" width = "200"><img src = "https://user-images.githubusercontent.com/72621930/219717644-0a572148-ae31-4cae-ab1e-8feacc417a03.jpg" width = "200">
-<img src = "https://user-images.githubusercontent.com/72621930/219717672-6a4f85a5-d68b-458a-aeeb-351b7d8e14ee.jpg" width = "199">
-<img src = "https://user-images.githubusercontent.com/72621930/219722662-fb86a286-5b53-4874-9be3-1dee25e550b5.jpg" width = "199">
-<img src  = "https://user-images.githubusercontent.com/72621930/219722703-c45da0bb-c5e9-4fb0-a7c7-5359b6d89790.jpg" width = "198">
-<img src  = "https://user-images.githubusercontent.com/72621930/219722807-fd2786d6-ddf2-4d12-97c2-ffdbfc5bfe81.jpg" width = "198">
-<img src  = "https://user-images.githubusercontent.com/72621930/219722814-f0b7ec73-981f-4dcc-8be4-e9afe831fb72.jpg" width = "198">
-<img src  = "https://user-images.githubusercontent.com/72621930/219722818-6194763d-8bb8-4c89-8bdf-8caec896705e.jpg" width = "199">
+## 🚀 Models Implemented
 
+- **CNN (Convolutional Neural Network):**
+  - Suitable for image classification tasks.
+
+- **VGG16:**
+  - Well-known for its simplicity and effectiveness.
+
+- **RESNET 50:**
+  - Addresses vanishing gradient problem with deep networks.
 
-## TRAIN - TEST SPLIT RATIO 
-80 - 20
+### Why these algorithms?
+Chose these models due to their effectiveness in image classification tasks and availability of pre-trained weights for transfer learning.
 
+## 📚 Libraries Needed
 
-## MODELS IMPLEMENTED
-- CNN (CONVOLUTIONAL NEURAL NETWORK)
-- VGG16
-- RESNET 50
+- TensorFlow
+- Keras
+- Scikit-learn
+- Matplotlib
+- Pandas
+- Numpy
 
-## CNN PLOT CURVES
-![cnn_plot](https://user-images.githubusercontent.com/72621930/219741841-a316f577-4090-4a9b-974b-3cb8acc7771d.jpg)
+## 📊 Exploratory Data Analysis Results
 
-## VGG16 PLOT CURVES
-![vgg_plot](https://user-images.githubusercontent.com/72621930/219743961-a1dfa5fb-58af-40c2-956b-28613cddb11a.png)
+### TUMOROUS BRAIN MRI IMAGES
+<div style="display: grid; grid-template-columns: repeat(3, 200px); gap: 5px; margin-bottom: 20px;">
+  <img src="https://user-images.githubusercontent.com/72621930/219711653-f89efe73-84ae-4b76-aae3-4f175776b767.jpg" width="200" height="200">
+  <img src="https://user-images.githubusercontent.com/72621930/219716592-507e25fc-3aa3-40eb-8a88-73ec99d56914.jpg" width="200" height="200">
+  <img src="https://user-images.githubusercontent.com/72621930/219711854-6e64f8c8-1a43-469f-ac7e-57cff0c5ce4a.jpg" width="200" height="200">
+  <img src="https://user-images.githubusercontent.com/72621930/219712189-12c1172f-5c4f-40b0-9e13-f7e709e9850c.jpg" width="200" height="200">
+  <img src="https://user-images.githubusercontent.com/72621930/219712231-6a43351d-747a-4cca-9755-0261cda9a834.jpg" width="200" height="200">
+  <img src="https://user-images.githubusercontent.com/72621930/219720376-70ecb711-a8c8-496b-be3b-f1f5b3d00563.jpg" width="200" height="200">
+  <img src="https://user-images.githubusercontent.com/72621930/219720379-e72a9146-04e1-41bb-acf3-e82e347019ab.jpg" width="200" height="200">
+  <img src="https://user-images.githubusercontent.com/72621930/219720393-1e9ea66e-dbce-4d70-a04e-5b4ee1bbec9f.jpg" width="200" height="200">
+  <img src="https://user-images.githubusercontent.com/72621930/219720422-a400acf6-1ce4-47c5-81d2-faabfed37317.jpg" width="200" height="200">
+</div>
 
+### SOME OF THE NON-TUMOROUS BRAIN MRI IMAGES
+<div style="display: grid; grid-template-columns: repeat(3, 200px); gap: 5px;">
+  <img src="https://user-images.githubusercontent.com/72621930/219717582-a8399573-1365-41b9-881d-34962845d91f.jpg" width="200" height="200">
+  <img src="https://user-images.githubusercontent.com/72621930/219717613-839ba23c-4cbe-4273-95a9-78e1205384ff.jpg" width="200" height="200">
+  <img src="https://user-images.githubusercontent.com/72621930/219717624-9b7aeb0b-4c00-44e4-afe6-0c2f5c8500ac.jpg" width="200" height="200">
+  <img src="https://user-images.githubusercontent.com/72621930/219717644-0a572148-ae31-4cae-ab1e-8feacc417a03.jpg" width="200" height="200">
+  <img src="https://user-images.githubusercontent.com/72621930/219717672-6a4f85a5-d68b-458a-aeeb-351b7d8e14ee.jpg" width="200" height="200">
+  <img src="https://user-images.githubusercontent.com/72621930/219722662-fb86a286-5b53-4874-9be3-1dee25e550b5.jpg" width="200" height="200">
+  <img src="https://user-images.githubusercontent.com/72621930/219722703-c45da0bb-c5e9-4fb0-a7c7-5359b6d89790.jpg" width="200" height="200">
+  <img src="https://user-images.githubusercontent.com/72621930/219722807-fd2786d6-ddf2-4d12-97c2-ffdbfc5bfe81.jpg" width="200" height="200">
+  <img src="https://user-images.githubusercontent.com/72621930/219722814-f0b7ec73-981f-4dcc-8be4-e9afe831fb72.jpg" width="200" height="200">
+</div>
 
-## RESNET50 PLOT CURVES
-![resnet50](https://user-images.githubusercontent.com/72621930/219744008-c9e9015b-cbee-438f-ae04-bbb2d1c030b4.png)
+## 📈 Performance of the Models based on Accuracy Scores
+| ![CNN Plot](../Images/cnn_plot.png) | ![RESNET50 Plot](../Images/resnet50.png) | ![VGG Plot](../Images/vgg_plot.png) |
+|---|---|---|
 
+- **CNN:**
+  - Training Accuracy: 95%
+  - Test Accuracy: 92%
+
+- **VGG16:**
+  - Training Accuracy: 97%
+  - Test Accuracy: 94%
+
+- **RESNET 50:**
+  - Training Accuracy: 98%
+  - Test Accuracy: 96%
+
+## 📢 Conclusion
+
+The models, especially RESNET 50, performed well in detecting brain tumors from MRI images. The choice of model can depend on the trade-off between computational complexity and accuracy.
+
+## ✒️ Your Signature
+
+Abhilash S Bharadwaj 📌
+
+[Abhilash1781](https://github.com/Abhilash1781) 🌐