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Koushim/
brain-ct-inceptionresnetv2

Name: brain-ct-inceptionresnetv2
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🧠 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.

📊 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.

🧠 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

🏁 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)

🔍 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.

🚀 Inference Example (Python - TensorFlow)

python

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])

Koushim/brain-ct-inceptionresnetv2