In today’s fast-evolving world of healthcare, early and accurate detection of diseases can save lives. Our project focuses on creating a comprehensive health risk detection system that combines quantum computing techniques with deep learning algorithms to analyze and classify a wide range of health conditions.
The system processes different types of medical data—including brain MRI scans, ECG signals, chest X-rays, spinal cord MRIs, and basic vitals like heart rate and oxygen levels. By doing so, it provides an all-in-one platform for identifying diseases such as brain tumors, cardiac issues, respiratory disorders, and neurological conditions.
What sets this system apart is its speed and efficiency. Traditional machine learning methods often take a lot of time to process and can fall short in accuracy. In contrast, our system has shown to classify hundreds of images in a matter of seconds while maintaining high reliability. By leveraging the strengths of quantum-inspired processing and deep learning models, we aim to build a medical diagnostic tool that is not only powerful but also practical for real-world use.
for brain tumor:
Summary Table:
Purpose Library Usage in Notebook Deep Learning TensorFlow, Keras Model building, training, evaluation Machine Learning scikit-learn Train/test split, classification metrics Quantum Technology PennyLane Quantum circuit simulation, quantum convolution layer Image Processing OpenCV, Matplotlib Image resizing, visualization Data Handling Pandas, h5py Loading data, labels, HDF5 file interaction Numerical Ops NumPy Array and math operations (via PennyLane's numpy)
precision recall f1-score support
1.0 0.93 0.84 0.88 113
2.0 0.92 0.97 0.94 213
3.0 0.98 0.97 0.97 132
accuracy 0.94 458
macro avg 0.94 0.93 0.93 458 weighted avg 0.94 0.94 0.94 458 Common Python Machine Learning Libraries scikit-learn (sklearn): For classic ML algorithms. pandas, numpy: For data manipulation and numerical computation. Deep Learning Libraries tensorflow or keras EfficientNetB0 matplotlib, seaborn: For plotting and visualization. Quantum Technology Libraries qiskit: IBM’s open-source quantum computing SDK. pennylane: Quantum machine learning library. cirq: Google’s quantum framework. Other Potential Libraries opencv-python: For image processing (since the dataset is ECG images). albumentations, imgaug: For image augmentation.
91/91 [==============================] - 173s 2s/step Classification Report (255x255 images):
precision recall f1-score support
0 1.00 1.00 1.00 814
1 1.00 1.00 1.00 716
2 1.00 1.00 1.00 852
3 0.99 1.00 1.00 516
accuracy 1.00 2898
macro avg 1.00 1.00 1.00 2898 weighted avg 1.00 1.00 1.00 2898
heart ecg