Merge pull request #1027 from Subhranil2004/pneumonia-classification

Pneumonia classification using Chest X-Ray

Pneumonia Classification using Chest X-Ray/README.md

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+# Pneumonia Classification using Chest X-Ray
+
+
+
+## 🎯Goal
+
+
+
+This project implements different ways of pneumonia prediction from Chest X-Ray images. Used custom built CNN model as well as various pre-trained models. The details of the performances of the above models can be found in the `Models` directory.  The best-fitting model is used for prediction in the web app.
+
+
+
+## 🧵 **Dataset**
+
+
+### Dataset  [Kaggle Link](https://www.kaggle.com/datasets/pcbreviglieri/pneumonia-xray-images/data), by Paulo Breviglieri
+
+*Adapted version of Paul Mooney's 'Chest X-Ray Images (Pneumonia)' [dataset](https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia), where the amount of observations for training and validation purposes was redistributed to allow for a more balanced machine learning exercise.
+
+
+## 🧮 **What I had done!**
+
+
+
+-  **Data Augmentation:** Used `ImageDataGenerator` class for augmenting images with the following params whose ranges were supplied:
+    - brightness_range : Varies brightness
+    - height_shift_range : Shifts the image vertically to a random value
+    - width_shift_range : Shifts the image horizontally to a random value
+    - rotation_range : Rotates the image to a random degree
+    - zoom_range : Zooms the image to a random degree
+
+    - preprocessing_function : Passed the preprocessing function of the respective pretrained model being used.
+-  **Built custom CNN model**
+-  **Transfer Learning Techniques:** VGG-16, ResNet50V2, MobileNet and Xception
+
+-  **Model Evaluation:** Used classification metrics and graphs for visualization
+
+## Live Demo
+
+- **For running on localhost** : Prepare the environment by installing all the requirements from `requirements.txt` 
+Then type the following command in the terminal : `streamlit run app.py`
+
+## Screenshots of app
+### Homepage
+![Homepage](./Images/Homepage.png)
+
+### Locally browse images 
+![Browse](./Images/Browse.png)
+
+### Prediction along with class probabilities
+![Prediction](./Images/Prediction.png)
+
+## 📚 **Libraries Needed**
+
+- tensorflow~=2.15.0
+- keras~=2.15.0
+- matplotlib
+- opencv-python
+- pillow
+- numpy
+- scikit-learn
+- seaborn
+
+
+ *Available in the `requirements.txt` file*
+
+
+## 📈  **Performance of the Models based on the Accuracy Scores**
+
+`VGG 16` was performing the best among the other pre-trained models.
+
+**VGG 16 classification metrics with graphs :**
+
+
+
+| ![graphs_VGG16](./Images/curves_vgg16.png) |
+|:--: |
+| *Loss and Accuracy curves* |
+
+
+| ![CM_VGG16](./Images/cm_vgg16.png) | ![Report_VGG16](./Images/report_vgg16.png) |
+|:--: |:--:|
+| *Confusion matrix* | *Classification report* |
+
+
+
+## 💻 **Steps for running this project locally**
+
+- I have originally used Python version `3.11` for this project.
+
+- Install the required packages from the `requirements.txt` file by using one of the following commands in the terminal:
+
+
+
+-  `pip install -r requirements.txt` (for `pip` environment)
+
+-  `conda install --yes --file requirements.txt` (for `conda` environment)
+
+
+
+## 📢 **Future Scope**
+
+
+The model accuracy could be improved so as to become fairly acceptable for clinical trials and real-life usage. 
+The `Pneumonia` class can be further classified into `Bacterial Pneumonia` or `Viral Pneumonia`. 
+
+
+## ✒️ **Signature**
+
+
+
+`Subhranil Nandy`  `GSSoC 2024 contributor`
+
+
+
+Connect with me :)
+
+-  `LinkedIn` : [subhranil-nandy](www.linkedin.com/in/subhranil-nandy)
+
+-  `GitHub` : [Subhranil2004](https://github.com/Subhranil2004)

Pneumonia Classification using Chest X-Ray/app.py

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+import streamlit as st
+import tensorflow as tf
+import time
+from PIL import Image, ImageOps
+import numpy as np
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.applications.vgg16 import preprocess_input
+
+# import json
+# import requests
+
+# from streamlit_lottie import st_lottie
+
+# Load the saved model
+model = tf.keras.models.load_model(r"Model\vgg_model.keras", compile=False)
+# TODO : train generalized model by inverting colour of half of the digits.
+
+
+def contrast_stretching(image):
+    # Calculate the minimum and maximum pixel values in the image
+    min_val = np.min(image)
+    max_val = np.max(image)
+
+    # Apply contrast stretching
+    stretched_image = ((image - min_val) / (max_val - min_val)) * 255  # min-max
+    stretched_image = stretched_image.astype(np.uint8)  # Convert to uint8
+
+    return stretched_image
+
+
+def preprocess_image(image_path):
+
+    # Load the image with the target size
+    img = image.load_img(image_path, target_size=(224, 224))
+
+    # Convert the image to a numpy array
+    img_array = image.img_to_array(img)
+
+    # Expand the dimensions to match the input shape (1, 224, 224, 3)
+    img_array = np.expand_dims(img_array, axis=0)
+
+    # Preprocess the image using VGG16 preprocess_input function
+    preprocessed_img = preprocess_input(img_array)
+
+    # Resize image to match model input size
+    # resized_image = equalized_image.resize((224, 224))
+
+    # Apply contrast stretching
+    return preprocessed_img
+
+
+# Define a function for model inference
+# @tf.function
+def predict(image):
+    # Open and preprocess the image
+    # img = Image.open(image)
+    preprocessed_image = preprocess_image(image)
+    # st.image(preprocessed_image, width=500, caption="pre")
+
+    # Convert image to NumPy array
+    img_array = np.array(preprocessed_image)
+    # img_array = np.expand_dims(img_array, axis=-1)  # Add batch dimension
+
+    # Make predictions using the loaded model
+    prediction = model.predict(img_array)
+
+    return prediction
+
+
+# Streamlit app code
+st.set_page_config(
+    page_title="Pneumonia Prediction from Chest X-Ray images",
+    layout="centered",
+    initial_sidebar_state="expanded",
+    menu_items={
+        # "Get Help": "https://github.com/Subhranil2004/digits-in-ink#live-demo",
+        # "Report a bug": "https://github.com/Subhranil2004/handwritten-digit-classification/issues",
+    },
+)
+
+# Sidebar
+with st.sidebar:
+    st.image(
+        "./images/image.jpg",
+        use_column_width=True,
+        output_format="JPEG",
+    )
+
+# st.sidebar.subheader(":blue[[Please use a desktop for the best experience.]]")
+
+st.sidebar.title("Pneumonia Prediction from Chest X-Ray images")
+st.sidebar.write(
+    "The model is trained on the ***Pneumonia X-Ray Images dataset*** from **Kaggle** and uses Convolutional Neural Network with Data augmentation."  # It has an exceptional accuracy rate of 99.45% on MNIST test dataset."
+)
+
+# st.sidebar.write(
+#     "There is always a scope for improvement and I would appreciate suggestions and/or constructive criticisms."
+# )
+
+st.sidebar.link_button("GitHub", "https://github.com/Subhranil2004")
+
+st.markdown(
+    f"""
+        <style>
+            .sidebar {{
+                width: 500px;
+            }}
+        </style>
+    """,
+    unsafe_allow_html=True,
+)
+
+# Main content
+
+st.title("Pneumonia Prediction")
+# st.subheader("from Chest X-Ray images")
+uploaded_file = st.file_uploader(
+    "Choose a chest X-Ray image...",
+    type=["jpg", "png", "bmp", "tiff"],
+)
+
+if uploaded_file is not None:
+    # Display the uploaded image with border
+    st.image(
+        uploaded_file,
+        caption="Uploaded Image",
+        width=300,
+        clamp=True,
+        # output_format="JPEG",
+    )
+
+    # Perform prediction
+    if st.button("Predict"):
+        result = predict(uploaded_file)
+        st.write("Done!")
+
+        # Display the prediction result
+        # max_index = round(result)
+        if result > 0.5:
+            output = ":red[PNEUMONIA  AFFECTED] ⚠️"
+            conf = result * 100
+        else:
+            output = "NORMAL ✅"
+            conf = (1 - result) * 100
+
+        st.write(f"Prediction :  {output}  [Confidence: :green[{conf[0][0]:.2f} %] ]")
+
+
+expander = st.expander("Some sample X-ray images to try with...", expanded=True)
+expander.write("Just drag-and-drop your chosen image above ")
+expander.image(
+    [
+        "./images/viral2.jpeg",
+        "./images/bacterial1.jpg",
+        "./images/viral1.jpg",
+        "./images/IM-0028-0001.jpeg",
+        "./images/person101_bacteria_484.jpeg",
+        "./images/person3_virus_17.jpeg",
+    ],
+    width=200,
+)
+# expander.write(
+#     "All images might not give the desired result as the *1st* prediction due to low contrast. Check the probability scores in such cases."
+# )
+# expander = st.expander("View Model Training and Validation Results")
+# expander.write("Confusion Matrix: ")
+# expander.image("./images/CNN_ConfusionMatrix.png", use_column_width=True)
+# expander.write("Graphs: ")
+# expander.image("./images/CNN_Graphs.png", use_column_width=True)
+
+# Footer
+st.write("\n\n\n")
+st.markdown("---")
+st.markdown(
+    f"""Drop in any discrepancies or give suggestions in `Report a bug` option within the `⋮` menu"""
+)
+
+st.markdown(
+    f"""<div style="text-align: right"> Developed by Subhranil Nandy </div>""",
+    unsafe_allow_html=True,
+)