Automated Leukemia Detection and Classification Using Deep Learning Technique

Description

The Automated Leukemia Detection and Classification project uses deep learning techniques to automatically classify microscopic blood cell images as healthy (HEM) or leukemia-affected (ALL). By employing Convolutional Neural Networks (CNNs), specifically VGG16 and MobileNet models, the system accurately identifies different stages of leukemia from optical images of blood cells.

The model is trained using data augmentation techniques to improve accuracy and prevent overfitting. The project highlights the power of transfer learning to enhance model performance, leveraging pretrained models for more efficient classification, while achieving high classification accuracy and robust performance in medical diagnostics.

Conclusion

This deep learning-based approach demonstrates the potential of CNNs in automating the classification of medical images, providing faster and more accurate diagnostics for leukemia. The use of transfer learning with models like VGG16 has proven to be highly effective, reducing the need for large datasets while maintaining excellent accuracy.

By integrating these technologies, the system offers a reliable and scalable solution for medical professionals, allowing for early detection and more effective treatment planning. This approach has the potential to be expanded for other medical imaging tasks, ultimately improving healthcare outcomes through automation and AI.