AI in Hematology with ResNet50 for Blood Cancer Classification
Blood cancers, including various forms of leukemia, are a critical area of medical research due to their complex nature and varying treatment approaches. Early and accurate diagnosis is essential for effective treatment, and deep learning models can assist in identifying different types of blood cancers. A model such as ResNet50 can be applied to classify blood cancer subtypes, improving diagnostic capabilities.
Implementation Highlights
- ResNet50 architecture is used for feature extraction and classification of blood cancer subtypes from medical data.
- Blood cancer images are resized to 200×200 pixels, optimizing the network for both speed and accuracy.
- Transfer learning is employed, using pre-trained weights to enhance performance, even with limited blood cancer-specific datasets.
- Augmentation techniques like flipping, rotation, and scaling are incorporated to ensure the model generalizes well to unseen data.
Outcome
The model efficiently classifies blood cancer types into benign, malignant pre-B, malignant pro-B, and malignant early pre-B categories. It aids clinicians in the early detection of blood cancers, providing an automated tool to assist in diagnosis. The system shows promising results in differentiating between various forms of blood cancer, supporting timely and accurate treatment decisions.