The Spreading Prediction and Severity Analysis of Blood Cancer Using Scale-Invariant Feature Transform

Blood cancer is an ever-growing global health concern. Early detection and accurate diagnosis are key to successful treatment and long-term prognosis of the disease. Despite various advances in medical technologies, current methods for blood cancer detection are far from perfect and often suffer from a lack of accuracy. This paper proposes the use of Scale-Invariant Feature Transform (SIFT) based approaches for predicting the spreading of blood cancer and assessing its severity. SIFT is a computer vision technique that extracts meaningful features from an image. Through SIFT, meaningful patterns in medical imaging datasets, such as those used for blood cancer detection, can be identified. The output from SIFT can then be used to build predictive models which can accurately predict the spread of the disease and its severity. Additionally, this paper explains how machine-learning algorithms and artificial neural networks can be used to further enhance the accuracy of these predictions. Finally, the proposed approach is validated using benchmark datasets and its accuracy is compared with existing methods for blood cancer prediction. The proposed model reached 89.71% accuracy, 89.25% precision, 90.71% recall and 89.40% f1-score results. The results show that prediction accuracy increases significantly when SIFT is used. It is concluded that SIFT can be a powerful tool for predicting blood cancer and estimating its severity.