Deep Learning approach for Automated Blood Cancer Cells detection

Abstract

A deep learning approach to automatic blood cancer diagnosis is proposed in this thesis. The aim of this research is to develop an efficient system for accurate early-stage diagnosis and thereby improve healthcare outcomes. The dataset of blood smear images was preprocessed using techniques such as noise removal, contrast stretching, and normalization to improve the feature extraction and model training process. Four deep learning models—Xception, InceptionV3, MobileNet, and ResNet50—were attempted. The best among them was InceptionV3 with 98%, followed by Xception and MobileNet with 97%. To achieve higher performance, two hybrid models were attempted: Hybrid Model 1, a combination of Xception, InceptionV3, and MobileNet, which resulted in 99%, and Hybrid Model 2, a combination of ResNet50 and VGG16, which resulted in 93%.These results underscore the significance of model architecture selection and preprocessing for accurate classification. The findings suggest that AI technology can greatly contribute towards the accuracy and timeliness of the diagnosis of leukemia, especially in poor-resource environments. The study shows the potential of deep learning algorithms and, more so, hybrid models for providing accurate, scalable, and efficient blood cancer detection for the final good of better clinical decision-making and better outcomes for patients.