| --- |
| license: mit |
| language: |
| - en |
| --- |
| # **XPathology-CNN 🔬** |
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| **XPathology-CNN** is an Explainable AI (XAI) computer vision model designed for the binary classification of H\&E-stained histopathology slides (**Benign vs. Adenocarcinoma**). |
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| This model served as the core visual engine for the broader **X-Pathology pipeline** (but now upgraded to a better more accurate model), which pairs CNN-based feature extraction with Large Language Models (LLMs) to generate plain-English and clinical dual-persona diagnostic reports. |
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| To see full indepth training and visualization with complete overview, visit my GitHub Profile: https://github.com/Muhammad-Hassan12/Colon-Cancer-Prediction-CNN-Model |
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| ## **🧠 Model Architecture** |
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| The model utilizes a transfer learning approach, leveraging a pre-trained VGG16 backbone fine-tuned for histopathological feature recognition. |
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| * **Base Model:** VGG16 (Pre-trained Convolutional Base) |
| * **Top Layers:** Flatten → Dense (256, ReLU) → Dense (1, Sigmoid) |
| * **Input Shape:** (256, 256, 3\) |
| * **Output:** Binary probability score (0 \= Benign, 1 \= Malignant) |
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| ## **📊 Performance & Training** |
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| The model exhibits highly stable convergence with minimal validation loss across 30 epochs, making its internal gradient structure highly optimal for feature extraction. |
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| | Metric | Value | |
| | :---- | :---- | |
| | **Training Set** | 4,200 images | |
| | **Validation Set** | 900 images | |
| | **Validation Accuracy** | 1.00 (100%) | |
| | **F1-Score** | 1.00 (Across both classes) | |
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| ## **⚠️ Disclaimer!** |
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| The model is very accurate, but as it is trained on the architecture of **VGG16** (which has *138 million trainable parameters*) the data is pretty limited for its true capability, so the model has become a little overconfident! |
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| Although it is very accurate on unseen data, but can be a little over-confident with the images that are not actually histo-reports 😂 (for example: *If tested on the image of dog, it will accuse it as it is either Benign or Adenocarcinoma with 95%+ confidence*) |
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| ## **🎯 Explainable AI (XAI) Integration** |
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| This model was explicitly fine-tuned to maintain stable gradients for **Grad-CAM** (Gradient-weighted Class Activation Mapping). |
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| By targeting the final convolutional layer (block5\_conv3), the model generates highly accurate heatmaps that highlight specific cellular structures—such as **nuclear atypia** or **irregular glandular formations**—that drive its diagnostic predictions. This transparency allows clinicians to verify the morphological basis of the AI's classification. |
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| ## **💻 How to Use** |
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| You can download and load this model directly into a TensorFlow/Keras environment using the huggingface\_hub library. |
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| ### **1\. Install Requirements** |
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| ```bash |
| pip install tensorflow huggingface\_hub |
| ``` |
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| ### **2\. Load the Model** |
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| ```bash |
| import tensorflow as tf |
| from tensorflow.keras import models, layers |
| from tensorflow.keras.applications import VGG16 |
| from huggingface\_hub import hf\_hub\_download |
| |
| \# 1\. Download the weights from Hugging Face |
| \# IMPORTANT: Replace 'YOUR\_USERNAME' with your actual Hugging Face handle |
| model\_path \= hf\_hub\_download(repo\_id="YOUR\_USERNAME/XPathology-CNN", filename="C\_DA\_1.h5") |
| |
| \# 2\. Reconstruct the architecture |
| conv\_base \= VGG16(weights=None, include\_top=False, input\_shape=(256, 256, 3)) |
| conv\_base.\_name \= 'vgg16\_base' |
| |
| cnn\_model \= models.Sequential(\[ |
| conv\_base, |
| layers.Flatten(name='flatten\_layer'), |
| layers.Dense(256, activation='relu', name='dense\_hidden'), |
| layers.Dense(1, activation='sigmoid', name='dense\_output') |
| \]) |
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| \# 3\. Load the weights |
| cnn\_model.load\_weights(model\_path) |
| print("XPathology Model Loaded Successfully\!") |
| ``` |
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| ## **⚠️ Disclaimer** |
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| This model is developed for **educational, portfolio, and research purposes only**. It is not intended for use in actual clinical diagnostics or patient care. All AI-assisted medical screenings must be reviewed by a certified human pathologist. |
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| [image1]: <data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABMAAAAXCAYAAADpwXTaAAAAt0lEQVR4XmNgGAWjgDpAQUGBQ05OLk1UVJQHXY4cwCgvL98KNNAYXYIsADIIaGAvkMmCLkcOYAR6twBoaByIjSIDlBAA2iRJClZSUgKaJTcfyJ6soqLCBzZIXFycGyhQDcSzSMVAw3YA6a9A3Aw0kB3FhaQAWVlZE6Ahq6WlpWXQ5UgCQAOEgQYtVlRUlEeXIxkADcoChnMEujjJAJRogYZNlZGRkUaXIwcwqqur84JodIlRMMAAAJV7J+RoCL8jAAAAAElFTkSuQmCC> |
