Csec-net: a novel deep features fusion and entropy-controlled firefly feature selection framework for leukemia classification.

Leukemia, a life-threatening form of cancer, poses a significant global health challenge affecting individuals of all age groups, including both children and adults. Currently, the diagnostic process relies on manual analysis of microscopic images of blood samples. In recent years, machine learning employing deep learning approaches has emerged as cutting-edge solutions for image classification problems. Thus, the aim of this work was to develop and evaluate deep learning methods to enable a computer-aided leukemia diagnosis. The proposed method is composed of multiple stages: Firstly, the given dataset images undergo preprocessing. Secondly, five pre-trained convolutional neural network models, namely MobileNetV2, EfficientNetB0, ConvNeXt-V2, EfficientNetV2, and DarkNet-19, are modified and transfer learning is used for training. Thirdly, deep feature vectors are extracted from each of the convolutional neural network and combined using a convolutional sparse image decomposition fusion strategy. Fourthly, the proposed approach employs an entropy-controlled firefly feature selection technique, which selects the most optimal features for subsequent classification. Finally, the selected features are fed into a multi-class support vector machine for the final classification. The proposed algorithm was applied to a total of 15562 images having four datasets, namely ALLID_B1, ALLID_B2, C_NMC 2019, and ASH and demonstrated superior accuracies of 99.64%, 98.96%, 96.67%, and 98.89%, respectively, surpassing the performance of previous works in the field.