Shehta Amr I, Nasr Mona, El Ghazali Alaa El Din M
Department of Information System, Faculty of Computers and Artificial Intelligence, Helwan University, Cairo, Egypt.
Department of Computer and Information Systems, Sadat Academy for Management Sciences, Cairo, Egypt.
Sci Rep. 2025 Jan 13;15(1):1889. doi: 10.1038/s41598-024-84475-0.
Blood cancer is among the critical health concerns among people around the world and normally emanates from genetic and environmental issues. Early detection becomes essential, as the rate of death associated with it is high, to ensure that the rate of treatment success is up, and mortality reduced. This paper focuses on improving blood cancer diagnosis using advanced deep learning techniques like ResNetRS50, RegNetX016, AlexNet, Convnext, EfficientNet, Inception_V3, Xception, and VGG19. Among the models assessed, ResNetRS50 had better accuracy and speed with minimal error rates compared with other state-of-the-arts. This work will exploit the power of ResNetRS50 in contributing to early detection and reducing bad outcomes for blood cancer patients. Blood cancer is currently one of the deadliest diseases worldwide, resulting from a combination of genetic and non-genetic factors. It stands as a leading cause of cancer-related deaths in both developed and developing nations. Early detection of cancer is pivotal in reducing mortality rates, as it increases the likelihood of successful treatment and potential cure. The objective is to decrease mortality rates through early diagnosis of blood cancer, thus offering individuals a better chance of survival from this disease.
血癌是全球人们最为关注的重大健康问题之一，通常由遗传和环境问题引发。由于与之相关的死亡率很高，早期检测至关重要，以确保提高治疗成功率并降低死亡率。本文着重于使用诸如ResNetRS50、RegNetX016、AlexNet、Convnext、EfficientNet、Inception_V3、Xception和VGG19等先进的深度学习技术来改进血癌诊断。在所评估的模型中，与其他现有技术相比，ResNetRS50具有更高的准确率和速度，且错误率最低。这项工作将利用ResNetRS50的优势，助力血癌患者的早期检测并减少不良后果。血癌目前是全球最致命的疾病之一，由遗传和非遗传因素共同导致。在发达国家和发展中国家，它都是癌症相关死亡的主要原因。癌症的早期检测对于降低死亡率至关重要，因为这增加了成功治疗和潜在治愈的可能性。目标是通过血癌的早期诊断降低死亡率，从而为患者提供更好的生存机会。
