Detection of Leukemia Disease Using Image Processing and Machine Learning

Abstract

Abstract: This paper presents a project that addresses Detection Of Leukemia Disease Using Image Processing and Machine Learning. This research proposes an approach that combines image processing techniques with machine learning algorithms to detect leukemia in microscopic blood smear images at a stage. The proposed method involves enhancing the images through preprocessing identifying the regions of interest through segmentation and extracting features using image processing algorithms. Afterward a machine learning model trained on a dataset of annotated images is employed to classify the samples as either positive or negative, for leukemia. The effectiveness of this approach is evaluated using metrics like accuracy, sensitivity and specificity. Experimental results show promising outcomes in detecting leukemia which could serve as a tool for healthcare professionals, in early diagnosis and treatment planning. By integrating this framework into practice it has the potential to improve efficiency and accuracy in diagnosing leukemia while ultimately leading to patient outcomes and enhanced healthcare management.