Brain CT Scan Classification with CNN

This project uses a Convolutional Neural Network (CNN) to classify brain CT scans as either normal or hemorrhage. It's a practical deep learning application for medical image classification, implemented using TensorFlow and Keras.

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🧾 Project Overview

Objective

The aim is to train a deep learning model that can accurately detect hemorrhages in CT scan images of the brain. This is critical in medical diagnostics, where rapid identification can be life-saving.

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Background

Why Deep Learning?

Deep Learning, particularly Convolutional Neural Networks (CNNs), excels at tasks involving image recognition. In medical imaging, CNNs can learn complex visual patterns to assist radiologists by automating the detection process.

Dataset

• 140 training images: 70 normal, 70 hemorrhage
• 60 validation images: 30 normal, 30 hemorrhage
• 10 test images for final evaluation
• Organized in directory format for use with Keras' ImageDataGenerator

Folder structure: data/ ├── head_ct_slices/ │ ├── train/ │ │ ├── normal/ │ │ └── hemorrhage/ │ ├── validate/ │ │ ├── normal/ │ │ └── hemorrhage/ │ └── test/

Model Architecture

We built a deep CNN using the following structure: Input (150x150x3) → Conv2D(16) + MaxPool → Conv2D(32) + MaxPool → Conv2D(64) + MaxPool → Conv2D(64) + MaxPool → Conv2D(128) + MaxPool → Flatten → Dense(256, ReLU) → Dense(1, Sigmoid) Each convolution layer extracts features. Pooling layers reduce spatial size and computation. Final layers interpret these features to classify the image.

Training Performance

Epochs: 10 Optimizer: Adam Loss: Binary Crossentropy Metric: Accuracy

Training output includes:

• accuracy and val_accuracy for performance tracking
• loss and val_loss to monitor potential overfitting

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Evaluation

After training, the model is tested on unseen test images. It predicts each image as either "normal" or "hemorrhage" based on a probability threshold of 0.5:

if prediction < 0.5:
    print("Hemorrhage")
else:
    print("Normal")

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Key Learnings

• How to use ImageDataGenerator for medical images
• Building and tuning CNN layers for feature extraction
• Interpreting model predictions
• Importance of validation and test sets in medical AI

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Author Notes

This project is designed as both a practical demo and a learning tool.
All steps and code are explained in plain language in the script file so that learners can follow along.