Scaled m-BonacciNet: A Recurrence-Guided Lightweight CNN for Efficient Medical Image Classification

Abstract

The m-Bonacci weighted sequence space [Formula] is introduced for m [≥]2, 1[≤]p < {infty}, and > 0. It is proved to be a Banach space isometrically isomorphic to lp, with an unconditional Schauder basis. The space is also reflexive and uniformly convex. Its strict inclusion chain, continuous dual, and asymptotic truncation estimate of order[Formula] are established. Based on this framework, Scaled m-BonacciNet is proposed as a lightweight CNN that rescales Fibonacci-Net channels using the dominant m-Bonacci root and employs depthwise separable convolutions. Tribonacci-Net achieved 96.09% accuracy and 0.9581 macro F1-score on the 15-class Brain MRI dataset using 0.85 million parameters and 0.366 GFLOPs. Pentanacci-Net attained 95.87% accuracy on the 44-class dataset, reducing parameters and FLOPs by 47.10% and 44.48%. Hexanacci-Net achieved 83.75% accuracy and 0.7941 macro F1-score on CKD ultrasound.

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BibTeX RIS

BibTeX

@misc{habib2026scaled,
  title = {{Scaled m-BonacciNet: A Recurrence-Guided Lightweight CNN for Efficient Medical Image Classification}},
  author = {G. Habib and S. Thakur and M. Shaban and R. Ahmad and I. A. Malik},
  howpublished = {medRxiv},
  year = {2026},
  month = oct,
  doi = {10.64898/2026.10.18.26362018},
  url = {https://www.medrxiv.org/content/10.64898/2026.10.18.26362018v1},
  note = {CC BY},
}

RIS

TY  - UNPB
TI  - Scaled m-BonacciNet: A Recurrence-Guided Lightweight CNN for Efficient Medical Image Classification
AU  - G. Habib
AU  - S. Thakur
AU  - M. Shaban
AU  - R. Ahmad
AU  - I. A. Malik
PY  - 2026
DA  - 2026/10/18
PB  - medRxiv
DO  - 10.64898/2026.10.18.26362018
UR  - https://www.medrxiv.org/content/10.64898/2026.10.18.26362018v1
AB  - The m-Bonacci weighted sequence space [Formula] is introduced for m [≥]2, 1[≤]p < {infty}, and > 0. It is proved to be a Banach space isometrically isomorphic to lp, with an unconditional Schauder basis. The space is also reflexive and uniformly convex. Its strict inclusion chain, continuous dual, and asymptotic truncation estimate of order[Formula] are established. Based on this framework, Scaled m-BonacciNet is proposed as a lightweight CNN that rescales Fibonacci-Net channels using the dominant m-Bonacci root and employs depthwise separable convolutions. Tribonacci-Net achieved 96.09% accuracy and 0.9581 macro F1-score on the 15-class Brain MRI dataset using 0.85 million parameters and 0.366 GFLOPs. Pentanacci-Net attained 95.87% accuracy on the 44-class dataset, reducing parameters and FLOPs by 47.10% and 44.48%. Hexanacci-Net achieved 83.75% accuracy and 0.7941 macro F1-score on CKD ultrasound.
ER  -

Abstract

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