Add Computed Tomography Analysis Using DL project

Computed Tomography Analysis using DL/Dataset/README.md

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+# CT Brain Object Detection Dataset
+
+## Overview
+The CT Brain Object Detection Dataset is a comprehensive collection of CT brain scans aimed at facilitating research in neurology, radiology, and oncology. It comprises images of brains afflicted with various conditions such as cancer, tumor, and aneurysm. These scans are invaluable resources for developing and assessing computer-based algorithms, machine learning models, and deep learning techniques for automated detection, diagnosis, and classification of brain diseases.
+
+## Dataset Details
+- **Format:** The dataset contains CT brain scans in two different formats: .jpg and .dcm (Digital Imaging and Communications in Medicine).
+- **Types of Brain Diseases:** The dataset encompasses three primary types of brain diseases:
+  1. Cancer
+  2. Tumor
+  3. Aneurysm
+
+## Accessing the Dataset
+The dataset is stored in the "files" folder, which is further organized into subfolders corresponding to the type of brain disease. Each subfolder contains CT scans of individuals with the respective condition in both .jpg and .dcm formats.
+
+Additionally, the dataset includes a file with the extension .csv, which provides essential information for each media file:
+- **dcm:** Link to access the .dcm file
+- **jpg:** Link to access the .jpg file
+- **type:** Name of the brain disease observed in the CT scan
+
+## Commercial Usage
+For commercial purposes, the full version of the dataset offers a more extensive collection of brain scans featuring individuals with diverse conditions. To obtain access to the complete dataset, interested parties can submit a request on TrainingData.
+
+## Acknowledgments
+We extend our gratitude to TrainingData for providing access to this valuable dataset, which holds significant potential for advancing research and innovation in the field of medical imaging analysis and diagnosis.
+
+For further inquiries or assistance, please feel free to contact us.
+
+---
+
+[Download Full Dataset](https://www.kaggle.com/datasets/trainingdatapro/computed-tomography-ct-of-the-brain)
+
+This README.md file is intended to provide a comprehensive overview of the CT Brain Object Detection Dataset, facilitating easy navigation and utilization for research and development purposes.

Computed Tomography Analysis using DL/Model/README.md

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+# Computed Tomography Analysis using DL
+
+## 🎯 Goal
+
+The primary objective of this project is to accurately classify medical diseases including tumors, cancer, and aneurysms from CT scan images by applying deep learning algorithms. The goal is to make it easier for medical practitioners to diagnose and treat patients.
+
+## 🧵 Dataset
+
+The dataset used in this project is sourced from Kaggle and can be accessed via the following link: [Computed Tomography (CT) of the Brain Dataset](https://www.kaggle.com/datasets/trainingdatapro/computed-tomography-ct-of-the-brain).
+
+## 🧾 Description
+
+This project involves the development of deep learning models to analyze CT scan images of the brain and classify them into specific categories based on medical conditions detected.
+
+## 🧮 What I Had Done!
+
+- Data collection and preprocessing.
+- Exploratory data analysis to gain insights into the dataset.
+- Implementation of deep learning models, including VGG16, ResNet50, and EfficientNetB7.
+- Evaluation of model performance on training, validation, and test datasets.
+- Comparison of model accuracies and selection of the best-performing model.
+
+## 🚀 Models Implemented
+
+- VGG16: Chosen for its simplicity and effectiveness in image classification tasks.
+- ResNet50: Selected for its ability to handle deep networks without vanishing gradient problems.
+- EfficientNetB7: Chosen for its balance of model depth, width, and resolution for optimal performance.
+
+## 📚 Libraries Needed
+
+- TensorFlow
+- Keras
+- Pandas
+- Matplotlib
+- Seaborn
+
+## 📊 Exploratory Data Analysis Results
+
+#### visualizations of the exploratory data analysis results 
+
+
+![class-distribution](../Images/output-class-distribution.png)
+![Image-Statistics](../Images/output-Image-Statistics.png)
+![sample-img-from-each-class](../Images/output-sample-img-from-each-class.png)
+
+
+
+## 📈 Performance of the Models based on the Accuracy Scores
+
+- **VGG16:**
+  - Accuracy: 100%
+- **ResNet50:**
+  - Accuracy: 100%
+- **EfficientNetB7:**
+  - Accuracy: 100%
+
+## 📢 Conclusion
+
+In summary, all models successfully classified CT scan pictures into the designated categories with an amazing 100% accuracy rate. Since every model worked just as well for this project, it is not required to select the one that fits the data the best.
+
+## ✒️ Your Signature
+
+Jaya Prakash Sangem
+
+[LinkedIn](https://www.linkedin.com/in/sangemjayaprakash)   
+[GitHub](https://github.com/Jaya-Prakash-17)
+

Computed Tomography Analysis using DL/Model/requirments.txt

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+numpy
+pandas
+matplotlib
+cv2
+PIL
+sklearn
+tensorflow
+IPython
+pydicom