Leukemia Blood Cancer Detection Using Mobile Net

Acute Lymphoblastic Leukaemia (ALL) is a life-threatening cancer affecting the blood and bone marrow, characterized by the abnormal production of white blood cells (WBC). Due to that the immune system is comprised, and the body becomes more vulnerable to infections as a result of these unhealthy cells crowding out healthy cells. Early and accurate detection is crucial for effective treatment and patient outcomes which is visible in the application proposed. The proposed model offers a robust solution by analysing microscopic images of blood samples to identify leukaemia and its stages. By leveraging a Convolutional Neural Network (CNN) architecture, the model can classify the presence of leukaemia and determine its stages whether it is early pre-B, pre-B or pro-B. If no cancer cells are detected, it displays as benign and provides additional information about the sample, ensuring comprehensive results (based on the accuracy, explained later). Moreover, the system includes an error-handling feature where if an incorrect or non-blood sample image is uploaded, it prompts the user with a message to upload correct image ensuring only valid images are processed. Additionally, the model provides basic information about each stage, helping healthcare professionals in explaining to their patients the better understanding the stage of the disease and make more informed treatment decisions. This application is designed exclusively for healthcare professionals to facilitate immediate and precise diagnosis, eliminating the time-intensive and error-prone process of manual examination which aids in timely treatment decisions, ultimately improving patient care and outcomes.