Utilizing MobileNetV2 for Enhanced Blood Cancer Detection

A growing number of people are dying every day as a result of acute lymphoblastic leukaemia. Those that survive suffer from a loss of vitality. The different shapes, patterns, and textures of ALL blast cells present a difficulty for the current artificial intelligence-based automated solutions. Around 1% of all blood cells are leukocytes, which are created in the bone marrow. The uncontrolled expansion of blood cells is one of the causes of blood cancer. In past times, the process was manually completed by a knowledgeable expert over the duration of a long amount of time. The proposed approach utilized a comprehensive dataset comprising high-quality blood cell images, encompassing various subtypes of blood cancer, to train and validate the MobileNetV2 architecture.