E-ConvNeXt: Efficient CNN Variants

• E-ConvNeXt is a family of CNN architectures featuring a lightweight variant with cross-stage partial connections and a hybrid model merging ConvNeXt with EfficientNet for diverse applications.
• The lightweight variant reduces parameters and FLOPs by up to 54% compared to ConvNeXt-tiny while maintaining competitive accuracy and faster inference speeds.
• The hybrid model leverages dual backbones to fuse complementary features, achieving near-perfect ROC-AUC for medical imaging tasks and improved diagnostic precision.

E-ConvNeXt refers to two distinct innovations within the ConvNeXt family of convolutional neural network (CNN) architectures: (1) a lightweight ConvNeXt variant with cross-stage partial connections designed for efficiency, as introduced in "E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections" (Wang et al., 28 Aug 2025); and (2) a hybrid model concatenating ConvNeXt and EfficientNet representations for improved medical image classification, as described in "A Hybrid ConvNeXt-EfficientNet AI Solution for Precise Falcon Disease Detection" (Panthakkan et al., 8 Jun 2025). Both approaches preserve core elements of ConvNeXt's architecture while introducing domain-adaptive modifications, targeted either at resource-constrained inference or at maximizing predictive accuracy in specialized settings.

1. Core Architecture and Variants

Efficient Lightweight Model: E-ConvNeXt with CSP Connections

E-ConvNeXt (Wang et al., 28 Aug 2025) integrates Cross Stage Partial Networks (CSPNet) into the ConvNeXt architecture to systematically reduce parameter count and computational cost:

• Stepped stem: A sequence of 2×2 and 3×3 convolutions enhances spatial detail preservation relative to ConvNeXt's original 4×4 stride-4 "patchify" stem.
• CSP stages: Each feature map is split along the channel axis after downsampling. One branch traverses ConvNeXt blocks (depthwise 7×7 convolution, expansion, channel attention), while the other bypasses. The two are concatenated and fused with a 1×1 convolution.
• Block optimization: LayerScale is replaced with channel attention (light Squeeze-and-Excitation, "ESE" block), increasing expressivity without significant computational overhead.
• Output structure: Progressive spatial downsampling and channel increases mirror standard ConvNeXt; stage/block/channels configuration is variant-specific (mini, tiny, small).

Hybrid Model: Concatenated ConvNeXt-EfficientNet (Editor's term)

The hybrid E-ConvNeXt (Panthakkan et al., 8 Jun 2025) consists of parallel ConvNeXt and EfficientNet branches:

• Dual backbone: Input image is simultaneously processed by ConvNeXt (patch-embedding, depthwise inverted bottleneck blocks, LayerNorm, GAP) and EfficientNet (MBConv blocks, compound scaling, GAP).
• Feature fusion: Output pooled vectors are concatenated and passed to a dense, shared classification head.
• Task-specific adaptation: Image size fixed at 128×128 for endoscopy images; original heads are removed to enable fusion at the feature level.

2. Mathematical Formulation

Lightweight E-ConvNeXt (CSP):

• Stage splitting and fusion:
  • For a feature map $X \in \mathbb R^{H \times W \times C_{in}}$:
  • Downsample and expand: $Y = \mathrm{Conv}_{2\times2,s=2}(X)$
  • Channel split: $$Y_1 = Y[:,:,:C_{mid}],\ Y_2 = Y[:,:,C_{mid}:C_{out}]$$, with $C_{mid} = \frac{C_{in} + C_{out}}{2}$
  • $Y_1$ passes through ConvNeXt-style blocks $\mathcal{B}$; $Y_2$ bypasses.
  • Fusion: $$Z = \mathrm{Conv}_{1\times1}([\,\mathcal{B}(Y_1); Y_2\,])$$
• ESE block: Channel attention is applied via global average pooling, linear projection, and rescaling with the sigmoid-activated attention weights.

Hybrid E-ConvNeXt (Concatenated):

• Branch outputs:
  • $$F_{ConvNeXt}(x) = Pool_{avg}(ConvNeXt_{stages}(Stem_C(x)))$$
  • $$F_{EffNet}(x) = Pool_{avg}(EfficientNet_{blocks}(Stem_E(x)))$$
• Fusion and classification:
  • $$F_{fusion}(x) = Concat(F_{ConvNeXt}(x), F_{EffNet}(x))$$
  • $z(x) = W F_{fusion}(x) + b$
  • Softmax activation $\hat y(x) = \phi(z(x))$
• Objective: Standard cross entropy: $$\mathcal L(y,\hat y) = -\sum_{i=1}^3 y_i \log \hat y_i$$

3. Empirical Results and Efficiency

Model Comparison

Variant	Params	FLOPs	Top-1 Acc. (%)
E-ConvNeXt-mini	7.6 M	0.93 G	78.3
E-ConvNeXt-tiny	13.2 M	2.04 G	80.6
E-ConvNeXt-small	19.4 M	3.12 G	81.9
ConvNeXt-tiny (baseline)	28.6 M	4.47 G	—

FLOPs and parameter reductions are approximately 54% relative to ConvNeXt-tiny, with a minimal accuracy penalty. When used as backbones in PP-YOLOE and YOLOv10, E-ConvNeXt-tiny achieves +1–5 % mAP gains on sonar imagery and up to 61.2% mAP on underwater optical images while remaining substantially more efficient (Wang et al., 28 Aug 2025).

Hybrid Model for Medical Imaging

Model	Accuracy	Precision	Recall	F1-Score
ConvNeXt (base)	0.87	0.88	0.87	0.87
EfficientNet (base)	0.94	0.95	0.95	0.94
Hybrid (Concat)	0.98	0.97	0.98	0.98

• ROC-AUC: Hybrid achieves AUC ≈ 0.99 for all classes; ROC curves show near-perfect separation between Normal, Liver Disease, and Aspergillosis (Panthakkan et al., 8 Jun 2025).

4. Ablation Studies and Design Choices

Ablation on the lightweight variant demonstrates:

• CSP integration: FLOPs decrease from 4.5G to 1.5G (∼66%); accuracy drops ∼1%.
• Transition parameter and stage-ratio tuning: Further complexity reduction with <1% degradation; rebalancing recovers much of the lost accuracy.
• Stepped stem and BN in blocks: Contribute +0.1% and +0.2% in Top-1, with ~25% runtime speedup employing BatchNorm vs LayerNorm.
• ESE channel attention: +0.8% improvement in accuracy.

For the hybrid model, the concatenation of ConvNeXt and EfficientNet feature vectors leverages complementary local and global representations, resulting in embeddings with improved linear separability and generalization, especially effective on small medical-imaging datasets (Panthakkan et al., 8 Jun 2025).

5. Practical Considerations and Implementation

• Inference speed: On a GTX 1080Ti, E-ConvNeXt-tiny achieves ~26.9 FPS with YOLOv10-M; per-block, BN-blocks attain ~550 FPS on 8×64×56×56 tensors (vs. ~447 FPS for LN-blocks).
• PyTorch implementation: Provided for E-ConvNeXt blocks, featuring depthwise and pointwise convolutional layers, BatchNorm, GELU activation, and ESE attention.
• Hybrid applications: In medical tasks, dual-branch architectures increase computational cost. The main limitations cited include doubled inference time and GPU memory, modest dataset size, and the need for further explainability methods prior to clinical use.

6. Implications, Limitations, and Future Perspectives

• Efficiency and scalability: E-ConvNeXt achieves a new state-of-the-art efficiency-accuracy trade-off among lightweight pure-CNN architectures and is robust to transfer for object detection (Wang et al., 28 Aug 2025).
• Hybridization efficacy: Concatenated hybrid models outperform single-backbone baselines in small-data and specialized domains, exploiting diverse inductive biases.
• Limitations: Both approaches are subject to deployment bottlenecks—device resource constraints for more complex hybrids; potential susceptibility to out-of-domain data with limited training size; and interpretability challenges.
• Future directions: Pruning, quantization, and knowledge distillation for mobile deployment; self-supervised pretraining on domain-specific data; and the integration of multimodal clinical information are proposed for further research (Panthakkan et al., 8 Jun 2025).

7. Related Work and Distinction

E-ConvNeXt (CSP) is distinguished by its architectural optimizations primarily aimed at reducing inference requirements. The hybrid E-ConvNeXt is separately motivated by application-driven late-fusion of independent CNN backbones for maximized diagnostic accuracy. Comparisons with alternative ConvNeXt adaptations such as EmoNeXt (Boudouri et al., 14 Jan 2025) highlight the modularity of ConvNeXt-based architectures, whose variants support integration of attention, spatial transformation, and late-fusion strategies across diverse problem domains.