Leukemia Prediction Using SVNN with a Nature-Inspired Optimization Technique

• Published in: Applications of Artificial Intelligence and Machine Learning pp. 305 - 319
• Main Authors: Das, Biplab Kanti, Das, Prasanta, Das, Swarnava, Dutta, Himadri Sekhar
• Format: Book Chapter
• Language: English
• Published: Singapore Springer Singapore
• Series: Lecture Notes in Electrical Engineering
• Subjects: Blast cells; Leukemia; SSA; Support vector neural network
• ISBN: 9789811630668; 9811630666
• ISSN: 1876-1100; 1876-1119
• DOI: 10.1007/978-981-16-3067-5_23

Blood smear examination is a basic test that helps us to diagnose various diseases. Presently, this is done manually, though automated and semiautomated blood cell counters are also in vogue. Automated counters are very costly, require specialized and proper maintenance to work, and trained manpower. Thus, in small laboratories and in periphery blood smears are mostly being done manually, followed by microscopic evaluation by trained medicos. Though this method is easily available and cost-effective, but there are always chances of variation in the result due to differences in the methods of preparation of slides and experience of the pathologist. To overcome the manual methods of blood smear examination, various studies are being undertaken which are targeted not only to identify different blood cells but also to specifically identify blast cells which are the cornerstone of diagnosis of acute leukemia by automated methods. This study proposes a leukemia detection method using salp swarm optimized support vector neural network (SSA-SVNN) classifier to identify leukemia in initial stages. Adaptive thresholding on LUV transformed image was used to perform segmentation of the preprocessed smear. From the segments, the features (shape, area, texture, and empirical mode decomposition) are extracted. Blast cells are detected using the proposed method based on the extracted features. The accuracy, specificity, sensitivity, and MSE of the proposed method are found to be 0.96, 1, 1, and 0.1707, respectively, implies that compared to other methods—KNN, ELM, Naive Bayes, SVM, there is improvement in leukemia detection.