Merge pull request #1108 from inkerton/main

COVID Detection from CXR Using Explainable CNN

Explainable-AI/Covid_Detection_from_Explainable_cnn/Readme.md

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+# COVID-19 Chest X-Ray Classification using Depthwise Separable Convolutional Neural Networks (CNN)
+
+This project focuses on building a convolutional neural network (CNN) model to classify Chest X-Ray (CXR) images into three categories: **COVID-19**, **Viral Infection**, and **Normal**. The model uses image enhancement techniques like **White Balance** and **CLAHE** (Contrast Limited Adaptive Histogram Equalization) for better image processing before classification.
+
+## Dataset
+
+The dataset used in this project consists of Chest X-Ray (CXR) images classified into three categories:
+- **COVID-19**: Images of chest X-rays from COVID-19 patients.
+- **Viral Infection**: Images of chest X-rays from patients with other viral infections.
+- **Normal**: Images of chest X-rays from healthy individuals.
+
+The dataset is stored in the following directories:
+- `/kaggle/input/covid-cxr-image-dataset-research/COVID_IEEE/covid/` for COVID-19 images.
+- `/kaggle/input/covid-cxr-image-dataset-research/COVID_IEEE/normal/` for normal images.
+- `/kaggle/input/covid-cxr-image-dataset-research/COVID_IEEE/virus/` for viral infection images.
+
+## Project Setup
+
+1. **Dependencies**: The project requires several Python libraries such as `numpy`, `cv2`, `matplotlib`, `PIL`, `keras`, and `sklearn`. These can be installed via pip:
+    ```bash
+    pip install numpy opencv-python matplotlib Pillow scikit-learn tensorflow
+    ```
+
+2. **Image Enhancement**: The dataset images are enhanced using **White Balance** and **CLAHE** to improve the clarity of the Chest X-rays for better feature extraction.
+
+3. **Model Architecture**: The model uses **Depthwise Separable Convolutional Neural Networks (CNN)**, which reduces the number of parameters while maintaining high performance for image classification tasks.
+
+## Data Preprocessing
+
+1. **White Balance**: Each image channel is processed by adjusting the intensity to standardize the color across images.
+    ```python
+    def wb(channel, perc=0.05):
+        mi, ma = (np.percentile(channel, perc), np.percentile(channel, 100.0-perc))
+        channel = np.uint8(np.clip((channel - mi) * 255.0 / (ma - mi), 0, 255))
+        return channel
+    ```
+
+2. **CLAHE (Contrast Limited Adaptive Histogram Equalization)**: This technique is applied to enhance the contrast in images, especially useful in medical imaging.
+    ```python
+    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(16, 16))
+    img_clahe1 = clahe.apply(gray_image)
+    ```
+
+3. **Resizing**: All images are resized to a fixed size of `224x224` pixels to ensure uniformity when feeding them into the model.
+
+4. **Normalization**: The pixel values of images are normalized by dividing by 255.0 to scale the values between 0 and 1.
+
+## Model Architecture
+
+The model architecture includes several convolutional layers, both traditional and depthwise separable, followed by dense layers for final classification:
+- **Conv2D Layers**: Initial convolution layers to extract features.
+- **MaxPooling Layers**: To reduce the spatial dimensions.
+- **SeparableConv2D Layers**: Depthwise separable convolutions to reduce parameters.
+- **BatchNormalization**: To normalize activations and improve training stability.
+- **Dropout**: To prevent overfitting.
+- **Dense Layers**: For classification of the images into three categories.
+
+```python
+inputs = Input(shape=(224, 224, 3))
+x = Conv2D(filters=4, kernel_size=(3, 3), activation='relu', padding='same')(inputs)
+x = MaxPool2D(pool_size=(2, 2))(x)
+...
+output = Dense(units=3, activation='softmax')(x)
+model = Model(inputs=inputs, outputs=output)
+```
+
+## Training and Evaluation
+
+1. **Model Compilation**: The model is compiled using the Adam optimizer and sparse categorical cross-entropy loss function.
+    ```python
+    model.compile(loss="sparse_categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
+    ```
+
+2. **Callbacks**: A `ModelCheckpoint` callback is used to save the best model based on validation loss during training.
+
+3. **Train-Test Split**: The data is split into training and testing sets using `train_test_split` from `sklearn`.
+
+4. **Data Augmentation**: An augmentation generator is used to artificially increase the size of the training data by applying random transformations.
+    ```python
+    trainAug = ImageDataGenerator(rotation_range=15, fill_mode="nearest")
+    ```
+
+## Results and Observations
+
+- **Normal CXR Images**: Clear lung patterns.
+- **Viral Infection CXR Images**: Slight congestion in the lungs.
+- **COVID-19 CXR Images**: Serious lung congestion.
+
+## Conclusion
+
+This project demonstrates how CNNs, particularly Depthwise Separable CNNs, can be used effectively to classify Chest X-ray images into categories such as COVID-19, Viral Infection, and Normal. The image enhancement techniques of White Balance and CLAHE significantly improve the quality of the input images, contributing to better model performance.
+
+## Future Work
+
+- Fine-tuning hyperparameters.
+- Exploring more advanced CNN architectures.
+- Integrating the model into a real-world application for automated CXR analysis.
+
+

Project-Structure.md

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     * [Toxicity](Deep_Learning/toxicity%20insult%20analyser/Toxicity.ipynb)
 
 ## Explainable-Ai
+  * Covid Detection From Explainable Cnn
+    * [Covid-Detection-From-Cxr-Using-Explainable-Cnn](Explainable-AI/Covid_Detection_from_Explainable_cnn/covid-detection-from-cxr-using-explainable-cnn.ipynb)
   * Explainable Ai Research Methods
     * [Xai](Explainable-AI/Explainable%20AI%20Research%20Methods/xai.ipynb)
   * Xai On Cancer Dataset