1. Introduction
Medical imaging, particularly CT scans, plays a critical role in disease diagnosis, yet identifying subtle pathological features remains a significant challenge for human experts and conventional AI. Early detection of these nuances is crucial for timely and effective treatment. This paper addresses the problem of enhancing the automated identification of such features using advanced deep learning. The primary models utilized in this study include the ConvNeXt base model and its proposed multi-branch variations.
2. Related Work
Previous research in medical image analysis has explored various convolutional neural networks (CNNs) for anomaly detection in CT scans, including ResNet, DenseNet, and standard Vision Transformers. While these models have achieved success in general feature extraction, they often face limitations in discerning subtle, low-contrast pathological indicators. Recent advancements in ConvNeXt architectures show promise in balancing computational efficiency with strong representation learning, offering a foundation for improved medical diagnostic tools.
3. Methodology
Our proposed methodology involves a novel multi-branch ConvNeXt architecture, where distinct branches are designed to extract features at different scales and receptive fields. Input CT scan slices are fed into these parallel ConvNeXt streams, each tailored with specific kernel sizes or dilation rates to capture fine-grained and global contextual information. The features from these branches are then fused through a sophisticated aggregation module, followed by classification layers. Training was conducted on a large, annotated dataset of CT scans, employing standard data augmentation techniques and optimized with the Adam optimizer.
4. Experimental Results
Experiments were conducted on a diverse dataset of CT scans containing various subtle pathologies, evaluating the model's performance using metrics such as accuracy, sensitivity, and F1-score. The multi-branch ConvNeXt model consistently outperformed single-branch ConvNeXt baselines and other state-of-the-art CNNs in detecting and classifying subtle features. The table below illustrates the superior performance across key metrics compared to existing methods.
| Model | Accuracy (%) | Sensitivity (%) | Specificity (%) |
|---|---|---|---|
| ResNet-50 | 88.2 | 85.5 | 90.1 |
| ConvNeXt-Base (Single) | 90.5 | 89.1 | 91.8 |
| Multi-branch ConvNeXt (Our Model) | 93.1 | 92.7 | 93.5 |
5. Discussion
The results confirm that the multi-branch ConvNeXt architecture significantly enhances the capability to identify subtle pathological features in CT scans, attributing its success to the parallel processing of multi-scale information. This improved sensitivity can lead to earlier and more accurate disease diagnoses, potentially impacting patient outcomes positively. Future work will focus on integrating attention mechanisms and validating the model on a wider range of pathology types and larger, more diverse datasets.