Efficient computer‐aided diagnosis technique for leukaemia cancer detection. Issue 17 (12th February 2021)

• Record Type: Journal Article
• Authors: Abdulla, Alan Anwer
• Is Part Of: IET image processing. Volume 14:Issue 17(2020)
• Page Start: 4435
• Page End: 4440
• Publication Date: 2021-02-12
• Subjects: blood -- feature extraction -- cancer -- CAD -- diseases -- image classification -- image segmentation -- medical image processing -- image texture -- cellular biophysics -- biomedical optical imaging; medical images -- digital image processing -- diseases identification -- detection purposes -- CAD system -- acute lymphoblastic leukaemia detection -- white blood cells -- microscopic blood image -- texture features -- segmented cells -- k‐nearest neighbour classifier techniques -- abnormal cells -- computer‐aided diagnosis technique -- leukaemia cancer detection; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2020.0978
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Computer‐aided diagnosis (CAD) is a common tool for the detection of diseases, particularly different types of cancers, based on medical images. Digital image processing thus plays a significant role in the processing and analysis of medical images for diseases identification and detection purposes. In this study, an efficient CAD system for the acute lymphoblastic leukaemia (ALL) detection is proposed. The proposed approach entails two phases. In the first phase, the white blood cells (WBCs) are segmented from the microscopic blood image. The second phase involves extracting important features, such as shape and texture features from the segmented cells. Eventually, on the extracted features, Naïve Bayes and k‐nearest neighbour classifier techniques are implemented to identify the segmented cells into normal and abnormal cells. The performance of the proposed approach has been assessed through comprehensive experiments carried out on the well‐known ALL‐IDB data set of microscopic blood images. The experimental results demonstrate the superior performance of the proposed approach over the state‐of‐the‐art in terms of accuracy rate in which achieved 98.7%.