Open Access

ARTICLE

Optimized Feature Selection for Leukemia Diagnosis Using Frog-Snake Optimization and Deep Learning Integration

Reza Goodarzi¹, Ali Jalali^1,*, Omid Hashemi Pour Tafreshi¹, Jalil Mazloum¹, Peyman Beygi²

1 Department of Electrical and Computer Engineering, Shahid Beheshti University, Tehran, 16589-53571, Iran
2 Department of Electrical and Computer Engineering, Shiraz University, Shiraz, 71946-84334, Iran

* Corresponding Author: Ali Jalali. Email:

(This article belongs to the Special Issue: Metaheuristic-Driven Optimization Algorithms: Methods and Applications)

Computers, Materials & Continua 2025, 84(1), 653-679. https://doi.org/10.32604/cmc.2025.062803

Received 28 December 2024; Accepted 16 April 2025; Issue published 09 June 2025

Abstract

Acute lymphoblastic leukemia (ALL) is characterized by overgrowth of immature lymphoid cells in the bone marrow at the expense of normal hematopoiesis. One of the most prioritized tasks is the early and correct diagnosis of this malignancy; however, manual observation of the blood smear is very time-consuming and requires labor and expertise. Transfer learning in deep neural networks is of growing importance to intricate medical tasks such as medical imaging. Our work proposes an application of a novel ensemble architecture that puts together Vision Transformer and EfficientNetV2. This approach fuses deep and spatial features to optimize discriminative power by selecting features accurately, reducing redundancy, and promoting sparsity. Besides the architecture of the ensemble, the advanced feature selection is performed by the Frog-Snake Prey-Predation Relationship Optimization (FSRO) algorithm. FSRO prioritizes the most relevant features while dynamically reducing redundant and noisy data, hence improving the efficiency and accuracy of the classification model. We have compared our method for feature selection against state-of-the-art techniques and recorded an accuracy of 94.88%, a recall of 94.38%, a precision of 96.18%, and an F1-score of 95.63%. These figures are therefore better than the classical methods for deep learning. Though our dataset, collected from four different hospitals, is non-standard and heterogeneous, making the analysis more challenging, although computationally expensive, our approach proves diagnostically superior in cancer detection. Source codes and datasets are available on GitHub.

---

Keywords

---