Koushim/brain-ct-inceptionresnetv2

🧠 Brain CT Classification with Keras (InceptionResNetV2)

This repository contains a fine-tuned TensorFlow/Keras image classification model for classifying Brain CT scans into three categories:

• Aneurysm
• Cancer
• Tumor

The model is developed using the color-enhanced medical imaging dataset. It leverages pretrained CNN architectures such as InceptionResNetV2 in TensorFlow/Keras, fine-tuned for medical image classification tasks.

The objective of this work is to demonstrate how colorization of grayscale CT scans can aid in improving model performance and interpretability in the field of medical diagnostics.

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📊 Dataset Information

• Original Dataset Name: Computed Tomography (CT) of the Brain - Medical Imaging (CT-Xray) Colorization New Dataset
• Source: Shuvo Kumar Basak - Kaggle
• Note: This dataset is publicly available for non-commercial research and educational purposes. The model does not include or redistribute the dataset.

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🧠 Model Architecture

• Framework: TensorFlow + Keras
• Model Type: InceptionResNetV2 (with ImageNet weights)
• Input Size: 299x299 (for InceptionResNetV2)
• Number of Classes: 3
• Training Strategy:
  • Global Average Pooling
  • Dense layers with ReLU and Dropout
  • Final output layer with softmax activation

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🏁 Training Summary

🔄 Preprocessing

• Images resized to 299x299 depending on the model
• Normalized to [0, 1] range

🧪 Training Details

• Loss Function: Categorical Crossentropy
• Optimizer: Adam
• Batch Size: 32
• Epochs: 30 (early stopping used)

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🔍 Intended Use

This model is built for:

• Educational projects in deep learning and medical imaging
• Research on CT-based brain abnormality classification
• Model interpretability using Grad-CAM++ visualization

⚠️ Not intended for clinical diagnosis or deployment without extensive validation and regulatory approval.

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🚀 Inference Example (Python - TensorFlow)

from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
import numpy as np

# Load model
model = load_model("brain_ct_classifier.h5")

# Preprocess
img = image.load_img("example_brain_ct.jpg", target_size=(299, 299))
img_array = image.img_to_array(img) / 255.0
img_array = np.expand_dims(img_array, axis=0)

# Predict
pred = model.predict(img_array)
class_idx = np.argmax(pred)
class_names = ["Aneurysm", "Cancer", "Tumor"]

print("Predicted class:", class_names[class_idx])