Blood Cancer Detection and Classification using ML Algorithms

The automatic detection of blood cancer, is a challenging yet crucial task for ensuring timely diagnosis and treatment. Leukemia affects the blood and bone marrow, and its symptoms often overlap with those of other conditions, making it difficult to detect. While traditional diagnostic methods, such as blood tests, bone marrow biopsies, and imaging techniques like CT scans and MRIs, are commonly used, they rely heavily on the expertise of trained medical professionals for accurate interpretation. However, these methods may not always provide reliable or consistent results. Machine learning algorithms offer a promising solution by automating the detection process. By analysing large datasets of patient information and medical images, these algorithms can identify subtle patterns and indicators of leukaemia that might be challenging for human experts to spot, improving both the speed and accuracy of detection.