Abstract
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.
Published Version
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