Work place: University College of Engineering, Jawaharlal Nehru Technological University, Hyderabad, Telangana, 500085, India
E-mail: chandrasekharaiahk@gmail.com
Website:
Research Interests: Wireless Networks, Computer Networks, Security Services
Biography
Dr Chandra Sekharaiah K. is a Professor Computer Science and Engineering of JNTU, Hyderabad. In his 27 Years of experience, he served in various capacities in teaching, institutional administration and system administration. He did his Ph.D from IIT Madras and later PostDoc from University of Jyvaskyla, Finland. After B.Tech., M.Tech., Ph.D. and PostDoc., He has 100 plus or minus research publications in various National and International Conferences and Journals. Dr Chandra Sekharaiah guided many research scholars for Ph D Degree. His research interests are in the realms of modeling, engineering and understanding of software systems related to distributed, security, language engineering, databases, wireless networks, web services, cognitive informatics and computer ergonomics domains of computing.
By Krishna Prasad Palli Edara Sreenivasa Reddy Chandra Sekharaiah K.
DOI: https://doi.org/10.5815/ijigsp.2024.04.07, Pub. Date: 8 Aug. 2024
In medical diagnosis, Artificial Intelligence (AI) has offered significant revolution, especially for cancers. Acute Myeloid Leukemia (AML) is a deadly blood cancer caused by the rapid growth of abnormal White Blood Cells (WBCs) in humans. Although AML classification is a popular area of research, existing detection methods utilize manual examination of microscopic blood samples, which includes high complexity and tedious. Therefore, this work presented a computerized deep learning model-based AML classification from peripheral blood stain images, which helps in earlier AML diagnosis. The processing steps followed in AML classification are Image Pre-processing, Localization of RoI (Region of Interest), Fusion-based Feature Extraction and Classification. First, the input image is pre-processed, which includes noise filtering, image resizing, and colour conversion. The noise in the image is filtered using normalized Gaussian filtering (NGF). Next, the image is resized into a standard size, and the RGB image is converted into CMYK colour space. Then, the RoI is identified using the Image Moment Localization (IML) technique. Next, the valuable multi-level dense features are extracted using DenseSqueeze Network, and multi-scale features are extracted using Dilated Convolution Spatial Pyramid Pooling (Dilated CSPP). Both these extracted features are fused using the element-wise summation. Finally, the Softmax classifier is used in the last layer to classify the classes of AML and the loss in the network is optimized using the Improved Artificial Fish Swarm (Improved AFS) algorithm. The proposed work results in 99% of accuracy, 98.5% of precision and 98.9% of F-score by using the AML-Cytomorphology LMU dataset.
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