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+title = "🧬 Detecting Cancer in Histopathology Images with CNNs"
+description = "A practical deep learning project for binary classification using the PatchCamelyon dataset."
+date = 2025-11-02
+[taxonomies]
+tags = ["machine_learning"]
+[extra]
+styles = ["notebooks.css", ]
++++
+
+## Overview
+
+This project explores the use of convolutional neural networks (CNNs) to detect
+metastatic cancer in histopathologic images of lymph node tissue. The task is
+framed as a binary classification problem, distinguishing between cancerous and
+non-cancerous image patches.
+
+The dataset, sourced from the PatchCamelyon (PCam) benchmark, offers a
+realistic simulation of the challenges faced by pathologists. With over 220,000
+labeled 96x96 RGB image patches, it strikes a balance between complexity and
+computational feasibility—making it ideal for experimentation on a single GPU.
+
+## Approach
+
+The workflow began with a thorough exploratory data analysis to understand the
+dataset’s structure, class distribution, and pixel intensity characteristics.
+Data augmentation and normalization were applied to improve generalization and
+training efficiency.
+
+A flexible CNN builder was implemented to test different architectures—ranging
+from simple to deeper and wider networks. After identifying the best-performing
+architecture, various regularization techniques were evaluated, including L1/L2
+penalties, dropout, and batch normalization.
+
+To ensure fair comparisons and mitigate overfitting, training was supported by
+callbacks such as early stopping, learning rate scheduling, and model
+checkpointing.
+
+## Results
+
+The deeper CNN architecture consistently outperformed the others, achieving a
+validation AUC of **0.9331**. Among regularization strategies, **additional
+batch normalization** provided the best boost in performance, pushing the final
+model’s validation AUC to **0.9878** when trained on the full dataset.
+
+The final model demonstrated strong generalization, with balanced precision and
+recall across both classes. Predictions on the test set were generated and
+compiled into a submission-ready format.
+
+## Reflections
+
+While the performance metrics are promising, the project also highlighted some
+challenges—particularly the variability in validation scores during early
+training. This variability diminished with larger datasets and longer training,
+suggesting that data volume plays a key role in stabilizing model performance.
+
+Future work could explore more advanced architectures, ensemble methods, or
+semi-supervised learning to further improve robustness and accuracy.
+
+***
+
+If you're curious about the details, the full notebook is embedded below 👇
+
+<!-- markdownlint-disable MD033 -->
+<iframe title="Spam Email Classification notebook" class="notebook-embed" src="notebook.html"></iframe>
+
+You can also view the notebook in [a separate page](notebook.html), or check it
+on [GitHub](https://github.com/Farzat07/Kaggle-Mini-Project-CNN-Cancer-Detection).