Investigative Study for Identification & Categorization of Leukaemia Cell

Leukaemia is a deadly disease based on cancer that affects people of all ages, involving young children, adults and is a leading cause of death worldwide. It is directly associated to White Blood Cells (WBC). This is followed by an increase in the quantity of immature cells and causes bone marrow and blood damage. As the result, a timely and accurate cancer diagnosis is important for successful therapy to improve survival chances. Currently, to diagnose this condition, a manual examination of blood samples collected using microscopic pictures is performed, which is typically very slow, time-consuming and inaccurate. Furthermore, in the microscopic investigation of leukemic cells look and form structures extremely similar to normal cells making identification more challenging. Deep learning using Convolutional Neural Networks (CNN) has given state-of-the-art methodologies for image categorization challenges in recent decades, yet there is still room for improvement in terms of efficacy, learning technique and performance. In this research survey we compare and study different available approaches for leukaemia detection including machine learning and deep-learning techniques. As a result, in this research investigation, we aim to develop a novel deep-learning algorithm variation to detect leukaemia illness by examining microscopic pictures of blood samples.