The fusion index is a critical metric for quantitatively assessing the transformation of in vitro muscle cells into myotubes in the biological and medical fields. Traditional methods for calculating this index manually involve the labor-intensive counting of numerous muscle cell nuclei in images, which necessitates determining whether each nucleus is located inside or outside the myotubes, leading to significant inter-observer variation. To address these challenges, this study proposes a three-stage process that integrates the strengths of pattern recognition and deep-learning to automatically calculate the fusion index. The experimental results demonstrate that the proposed process achieves significantly higher performance in cell nuclei detection and classification, with an F1-score of 0.953, whereas traditional object detection methods achieve less than 0.5. In addition, the fusion index obtained using the proposed method is closely aligned with the human-assessed values, showing minimal discrepancy and strong agreement with human evaluations. This process is incorporated into the development of "SEPO-FI" as public software, automating cell detection and classification to enable effective fusion index calculation and broaden access to this methodology within the scientific community.