如今，医生严重依赖医学成像来识别异常情况。对这些异常进行正确分类使他们能够采取明智的行动，从而实现早期诊断和治疗。本文介绍了“EfficientKNN”模型，这是一种新颖的混合深度学习方法，它将 EfficientNetB3 的高级特征提取功能与 k-近邻 (k-NN) 算法的简单性和有效性相结合。最初，在 ImageNet 上预先训练的 EfficientNetB3 被重新用作特征提取器。随后，应用 GlobalAveragePooling2D 层，然后进行可选的主成分分析 (PCA)，以在保留关键信息的同时降低维度。当认为有必要时，有选择地使用 PCA。然后使用优化的 k-NN 算法对提取的特征进行分类，并通过细致的交叉验证进行微调。我们的模型使用包含良性、恶性和正常医学图像的精选数据集进行了严格的训练。采用数据增强技术（包括旋转、移位、翻转和缩放）来帮助模型概括并有效处理新的、看不见的数据。为了增强模型识别准确预测所需的重要特征的能力，使用分割和叠加技术对数据集进行了细化。训练使用了一组优化算法（SGD、Adam 和 RMSprop），超参数设置为学习率为 0.00045，批量大小为 32，最多 120 个时期，并通过提前停止来防止过度拟合。结果表明： EfficientKNN 模型在准确性、精度和 F1 分数方面优于 VGG16、AlexNet 和 VGG19 等传统模型。此外，与单独使用 EfficientNetB3 相比，该模型显示出更好的结果。 EfficientKNN 模型在多次测试中实现了 100% 的准确率，在现实世界的诊断应用中具有巨大的潜力。这项研究强调了模型的可扩展性、云存储的有效利用和实时预测能力，同时最大限度地减少了计算需求。通过将 EfficientNetB3 深度学习架构的优势与 k-NN 的可解释性相结合，EfficientKNN 在医学领域取得了重大进步图像分类，有望提高诊断准确性和临床适用性。版权所有 © 2024 Elsevier Ltd。保留所有权利。

Today, doctors rely heavily on medical imaging to identify abnormalities. Proper classification of these abnormalities enables them to take informed actions, leading to early diagnosis and treatment. This paper introduces the "EfficientKNN" model, a novel hybrid deep learning approach that combines the advanced feature extraction capabilities of EfficientNetB3 with the simplicity and effectiveness of the k-Nearest Neighbors (k-NN) algorithm. Initially, EfficientNetB3, pre-trained on ImageNet, is repurposed to serve as a feature extractor. Subsequently, a GlobalAveragePooling2D layer is applied, followed by an optional Principal Component Analysis (PCA) to reduce dimensionality while preserving critical information. PCA is used selectively when deemed necessary. The extracted features are then classified using an optimized k-NN algorithm, fine-tuned through meticulous cross-validation.Our model underwent rigorous training using a curated dataset containing benign, malignant, and normal medical images. Data augmentation techniques, including rotations, shifts, flips, and zooms, were employed to help the model generalize and efficiently handle new, unseen data. To enhance the model's ability to identify the important features necessary for accurate predictions, the dataset was refined using segmentation and overlay techniques. The training utilized an ensemble of optimization algorithms-SGD, Adam, and RMSprop-with hyperparameters set at a learning rate of 0.00045, a batch size of 32, and up to 120 epochs, facilitated by early stopping to prevent overfitting.The results demonstrate that the EfficientKNN model outperforms traditional models such as VGG16, AlexNet, and VGG19 in terms of accuracy, precision, and F1-score. Additionally, the model showed better results compared to EfficientNetB3 alone. Achieving a 100 % accuracy rate on multiple tests, the EfficientKNN model has significant potential for real-world diagnostic applications. This study highlights the model's scalability, efficient use of cloud storage, and real-time prediction capabilities, all while minimizing computational demands.By integrating the strengths of EfficientNetB3's deep learning architecture with the interpretability of k-NN, EfficientKNN presents a significant advancement in medical image classification, promising improved diagnostic accuracy and clinical applicability.Copyright © 2024 Elsevier Ltd. All rights reserved.