access icon free Efficient computer-aided diagnosis technique for leukaemia cancer detection

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%.

Inspec keywords: cancer; medical image processing; feature extraction; diseases; CAD; image segmentation; image texture; cellular biophysics; blood; image classification; biomedical optical imaging

Other keywords: medical images; leukaemia cancer detection; digital image processing; segmented cells; microscopic blood image; detection purposes; k-nearest neighbour classifier techniques; abnormal cells; texture features; CAD system; computer-aided diagnosis technique; acute lymphoblastic leukaemia detection; diseases identification; white blood cells

Subjects: Computer vision and image processing techniques; Patient diagnostic methods and instrumentation; Cellular biophysics; Optical and laser radiation (medical uses); Optical and laser radiation (biomedical imaging/measurement); Image recognition; Biology and medical computing

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