Abstract
Acute myeloid leukemia (AML) is recognized as a highly aggressive cancer that affects the bone marrow and blood, making it the most lethal type of leukemia. The detection of AML through medical imaging is challenging due to the complex structural and textural variations inherent in bone marrow images. These challenges are further intensified by the overlapping intensity between leukemia and non-leukemia regions, which reduces the effectiveness of traditional predictive models. This study presents a novel artificial intelligence framework that utilizes residual block merging vision transformers, convolutions, and advanced object detection techniques to address the complexities of bone marrow images and enhance the accuracy of AML detection. The framework integrates residual learning-based vision transformer (ReLViT) blocks within a bottleneck architecture, harnessing the combined strengths of residual learning and transformer mechanisms to improve feature representation and computational efficiency. Tailored data pre-processing strategies are employed to manage the textural and structural complexities associated with low-quality images and tumor shapes. The framework's performance is further optimized through a strategic weight-sharing technique to minimize computational overhead. Additionally, a generative adversarial network (GAN) is employed to enhance image quality across all AML imaging modalities, and when combined with a You Only Look Once (YOLO) object detector, it accurately localizes tumor formations in bone marrow images. Extensive and comparative evaluations have demonstrated the superiority of the proposed framework over existing deep convolutional neural networks (CNN) and object detection methods. The model achieves an F1-score of 99.15%, precision of 99.02%, and recall of 99.16%, marking a significant advancement in the field of medical imaging.