Title: Optimisation with deep learning for leukaemia classification in federated learning

Authors: Smritilekha Das; Padmanaban Kuppan

Addresses: Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India ' Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India

Abstract: The most common kind of blood cancer in people of all ages is leukaemia. The fractional mayfly optimisation (FMO) based DenseNet is proposed for the identification and classification of leukaemia in federated learning (FL). Initially, the input image is pre-processed by adaptive median filter (AMF). Then, cell segmentation is done using the Scribble2label. After that, image augmentation is accomplished. Finally, leukaemia classification is accomplished utilising DenseNet, which is trained using the FMO. Here, the FMO is devised by merging the mayfly algorithm (MA) and the fractional concept (FC). Following local training, the server performs local updating and aggregation using a weighted average by RV coefficient. The results showed that FMO-DenseNet attained maximum accuracy, true negative rate (TNR) and true positive rate (TPR) of 94.3%, 96.5% and 95.3%. Moreover, FMO-DenseNet gained minimum mean squared error (MSE) and root mean squared error (RMSE) of 5.7%, 9.2% and 30.4%.

Keywords: leukaemia; federated learning; fractional concept; mayfly algorithm; DenseNet.

DOI: 10.1504/IJBRA.2024.142549

International Journal of Bioinformatics Research and Applications, 2024 Vol.20 No.6, pp.556 - 583

Received: 13 Nov 2023
Accepted: 11 Mar 2024
Published online: 08 Nov 2024 *

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