Transfer Learning For White Blood Cell Leukemia Detection In Image Processing - AL-Badareen , Anas; Harasees , Alaa; Bataina , Norma; Altarawneh , Khalid; Altarawni , Ibrahim; M. D. , Ervina Bejko (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Leukemia, a type of blood cancer, requires accurate and early detection for effective treatment. White blood cell (WBC) abnormalities play a crucial role in diagnosing leukemia. However, manual examination of blood smears to detect abnormal WBCs is time-consuming and prone to human error. Transfer learning, a technique that leverages pre-trained deep neural networks, has shown promise in improving the accuracy and efficiency of leukemia detection. In this study, author explore the application of transfer learning using three popular deep neural network architectures: InceptionV3, Squeeze Net, and ResNet50, for WBC detection in leukemia. Author utilize a large dataset of microscopic blood smear images, consisting of both normal and abnormal WBCs, to train and fine-tune these pre-trained networks. By leveraging the knowledge and features learned from massive image datasets, transfer learning enables us to effectively extract relevant features from the blood smear images and enhance the accuracy of leukemia detection. The trained models are evaluated on a separate test set of blood smear images, and their performance metrics, including accuracy, precision, recall, and F1-score, are measured and compared. Additionally, we assess the computational efficiency of the models in terms of inference time, which is crucial for real-time diagnosis.