Open Access

ARTICLE

A Firefly Algorithm-Optimized CNN–BiLSTM Model for Automated Detection of Bone Cancer and Marrow Cell Abnormalities

Ishaani Priyadarshini^*

School of Electrical Engineering & Computer Science, Washington State University, Everett, WA 98201, USA

* Corresponding Author: Ishaani Priyadarshini. Email:

(This article belongs to the Special Issue: Nature-Inspired Optimization & Applications in Computer Science: From Particle Swarms to Hybrid Metaheuristics)

Computers, Materials & Continua 2026, 86(3), 64 https://doi.org/10.32604/cmc.2025.072343

Received 25 August 2025; Accepted 28 October 2025; Issue published 12 January 2026

Abstract

Early and accurate detection of bone cancer and marrow cell abnormalities is critical for timely intervention and improved patient outcomes. This paper proposes a novel hybrid deep learning framework that integrates a Convolutional Neural Network (CNN) with a Bidirectional Long Short-Term Memory (BiLSTM) architecture, optimized using the Firefly Optimization algorithm (FO). The proposed CNN-BiLSTM-FO model is tailored for structured biomedical data, capturing both local patterns and sequential dependencies in diagnostic features, while the Firefly Algorithm fine-tunes key hyperparameters to maximize predictive performance. The approach is evaluated on two benchmark biomedical datasets: one comprising diagnostic data for bone cancer detection and another for identifying marrow cell abnormalities. Experimental results demonstrate that the proposed method outperforms standard deep learning models, including CNN, LSTM, BiLSTM, and CNN–LSTM hybrids, significantly. The CNN-BiLSTM-FO model achieves an accuracy of 98.55% for bone cancer detection and 96.04% for marrow abnormality classification. The paper also presents a detailed complexity analysis of the proposed algorithm and compares its performance across multiple evaluation metrics such as precision, recall, F1-score, and AUC. The results confirm the effectiveness of the firefly-based optimization strategy in improving classification accuracy and model robustness. This work introduces a scalable and accurate diagnostic solution that holds strong potential for integration into intelligent clinical decision-support systems.

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