https://github.com/akash1070/predicting-lung-disease-with-computer-vision

Predicting Lung Disease With CV
https://github.com/akash1070/predicting-lung-disease-with-computer-vision

computer-vision inceptionv3 keras-models lung-diseases vgg16 vgg19

Last synced: 7 months ago
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Predicting Lung Disease With CV

• Host: GitHub
• URL: https://github.com/akash1070/predicting-lung-disease-with-computer-vision
• Owner: Akash1070
• Created: 2022-09-24T03:22:00.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2022-09-24T04:25:25.000Z (about 3 years ago)
• Last Synced: 2025-01-29T11:52:19.532Z (9 months ago)
• Topics: computer-vision, inceptionv3, keras-models, lung-diseases, vgg16, vgg19
• Language: Jupyter Notebook
• Homepage:
• Size: 7.81 KB
• Stars: 2
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# **Predicting Lung Disease With CV**

![Python 3.6](https://img.shields.io/badge/Python-3.6-brightgreen.svg)

Download Additional Files : https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia

• This repository consists of files required to deploy a Lung Disease Predictor With CV

• Please do ⭐ the repository, if it helped you in anyway.

## Authors

- [@Akash Kumar Jha](https://github.com/Akash1070)

## Deployment

  1. Download the necessary files. #images and videos

  2. Import the following Libraries below mentioned.

  3. Re-size all the images to this.

  4. Import the Vgg 16 library as shown below and add preprocessing layer to the front of VGG.

  5. tell the model what cost and optimization method to use.

  6. Use the Image Data Generator to import the images from the dataset.

  7. Make sure you provide the same target size as initialied for the image size.

  8. fit the model & Run the cell. It will take some time to execute.

  9. plot the loss & Accuracy.

  10. save it as a h5 file

  

## Installation

To install the libraries used in this project. Follow the 

below steps:

```bash

from keras.layers import Input, Lambda, Dense, Flatten

from keras.models import Model

#from keras.applications.resnet50 import ResNet50

from keras.applications.vgg16 import VGG16

from keras.applications.vgg16 import preprocess_input

from keras.preprocessing import image

from keras.preprocessing.image import ImageDataGenerator

from keras.models import Sequential

import numpy as np

from glob import glob

import matplotlib.pyplot as plt

from keras.preprocessing.image import ImageDataGenerator

from keras.applications.vgg19 import VGG19

from keras.applications.inception_v3 import InceptionV3

```

    

## Running Flask Api

To run tests, run the following command

```bash

  python app.py

```

## 🚀 About Me

Data Scientist Enthusiast | Petroleum Engineer Graduate | Solving Problems Using Data 

# Hi, I'm Akash! 👋

## 🔗 Links

[![github](https://img.shields.io/badge/github-000?style=for-the-badge&logo=ko-fi&logoColor=white)](https://github.com/Akash1070)

[![linkedin](https://img.shields.io/badge/linkedin-0A66C2?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/akashkumar107/)

## Tech Stack

![Logo](https://businesstoys.in/assets/programs/full-stack-data-science-professional-program/tools.png)

## Other Me

👩‍💻 I’m interested in Petroleum Engineering

🧠 I’m currently learning Data Scientist | Data Analytics | Business Analytics

👯‍♀️ I’m looking to collaborate on Ideas & Data

## 🛠 Skills

1. Data Scientist

2. Data Analyst

3. Business Analyst

4. Machine Learning 

## Future Plans 

⚡️ Looking forward to help drive innovations into your company as a Data Scientist

⚡️ Looking forward to offer more than I take and leave the place better than i found