| Title |
A novel deep learning based approach with hyperparameter selection using grey wolf optimization for leukemia classification and hematologic malignancy detection |
| Authors |
Ur Rehman, Shams ; DamaĊĦevicius, Robertas ; Al Sukhni, Hassan ; Aljohani, Abeer ; Hamza, Ameer ; Mohammed Alsekait, Deema ; AbdElminaam, Diaa Salama |
| DOI |
10.7717/peerj-cs.3160 |
| Full Text |
|
| Is Part of |
PeerJ computer science.. London : PeerJ. 2025, vol. 11, art. no. e3160, p. 1-30.. ISSN 2376-5992 |
| Keywords [eng] |
Customized CNN ; Grey wolf optimization ; Leukemia cancer ; Self-attention ; Vision transformer |
| Abstract [eng] |
Traditional diagnostic methods of leukemia, a blood cancer disease, are based on visual assessment of white cells in microscopic peripheral blood smears, and as a result, they are arbitrary, laborious, and susceptible to errors. This study proposes a new automated deep learning-based framework for accurately classifying leukemia cancer. A novel lightweight algorithm based on the hyperbolic sin function has been designed for contrast enhancement. In the next step, we proposed a customized convolutional neural network (CNN) model based on a parallel inverted dual self-attention network (PIDSAN4), and a tiny16 Vision Transformer (ViT) has been employed. The hyperparameters were tuned using the grey wolf optimization and then used to train the models. The experiment is carried out on a publicly available leukemia microscopic images dataset, and the proposed model achieved 0.913 accuracy, 0.892 sensitivity, 0.925 specificity, 0.883 precision, 0.894 F-measure, and 0.901 G-mean. The results were compared with state-of-the-art pre-trained models, showing that the proposed model improved accuracy. |
| Published |
London : PeerJ |
| Type |
Journal article |
| Language |
English |
| Publication date |
2025 |
| CC license |
|
