Gastrointestinal (GI) diseases are prevalent medical conditions that require accurate and timely diagnosis for effective treatment. To address this, we developed the Multi-Fusion Convolutional Neural Network (MF-CNN), a deep learning framework that strategically integrates and adapts elements from six deep learning models, enhancing feature extraction and classification of GI diseases from endoscopic images. The MF-CNN architecture leverages truncated and partially frozen layers from existing models, augmented with novel components such as Auxiliary Fusing Layers (AuxFL), Fusion Residual Block (FuRB), and Alpha Dropouts (αDO) to improve precision and robustness. This design facilitates the precise identification of conditions such as ulcerative colitis, polyps, esophagitis, and healthy colons. Our methodology involved preprocessing endoscopic images sourced from open databases, including KVASIR and ETIS-Larib Polyp DB, using adaptive histogram equalization (AHE) to enhance their quality. The MF-CNN framework supports detailed feature mapping for improved interpretability of the model’s internal workings. An ablation study was conducted to validate the contribution of each component, demonstrating that the integration of AuxFL, αDO, and FuRB played a crucial part in reducing overfitting and efficiency saturation and enhancing overall model performance. The MF-CNN demonstrated outstanding performance in terms of efficacy, achieving an accuracy rate of 99.25%. It also excelled in other key performance metrics with a precision of 99.27%, a recall of 99.25%, and an F1-score of 99.25%. These metrics confirmed the model’s proficiency in accurate classification and its capability to minimize false positives and negatives across all tested GI disease categories. Furthermore, the AUC values were exceptional, averaging 1.00 for both test and validation sets, indicating perfect discriminative ability. The findings of the P-R curve analysis and confusion matrix further confirmed the robust classification performance of the MF-CNN. This research introduces a technique for medical imaging that can potentially transform diagnostics in gastrointestinal healthcare facilities worldwide.

中文翻译：

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开发多融合卷积神经网络（MF-CNN）以增强内窥镜图像分析中胃肠道疾病的诊断

胃肠道 (GI) 疾病是一种普遍存在的疾病，需要准确、及时的诊断才能进行有效的治疗。为了解决这个问题，我们开发了多重融合卷积神经网络（MF-CNN），这是一种深度学习框架，可以战略性地集成和调整来自六种深度学习模型的元素，从而增强内窥镜图像中胃肠道疾病的特征提取和分类。 MF-CNN 架构利用现有模型中的截断层和部分冻结层，并通过辅助融合层 (AuxFL)、融合残差块 (FuRB) 和 Alpha Dropouts (αDO) 等新颖组件进行增强，以提高精度和鲁棒性。这种设计有助于精确识别溃疡性结肠炎、息肉、食管炎和健康结肠等病症。我们的方法涉及对来自开放数据库（包括 KVASIR 和 ETIS-Larib Polyp DB）的内窥镜图像进行预处理，并使用自适应直方图均衡化 (AHE) 来提高图像质量。 MF-CNN 框架支持详细的特征映射，以提高模型内部工作的可解释性。进行了消融研究来验证每个组件的贡献，证明 AuxFL、αDO 和 FuRB 的集成在减少过度拟合和效率饱和以及提高整体模型性能方面发挥了至关重要的作用。 MF-CNN在功效方面表现出色，准确率达到99.25%。它在其他关键性能指标方面也表现出色，准确率为 99.27%，召回率为 99.25%，F1 分数为 99.25%。这些指标证实了该模型在准确分类方面的熟练程度及其在所有测试的胃肠道疾病类别中最大限度地减少假阳性和阴性的能力。此外，AUC 值非常出色，测试集和验证集的平均值均为 1.00，表明具有完美的辨别能力。 PR曲线分析和混淆矩阵的结果进一步证实了MF-CNN稳健的分类性能。这项研究引入了一种医学成像技术，有可能改变全球胃肠道医疗机构的诊断方法。