Nowadays medical imaging plays a vital role in diagnosing the various types of diseases among patients across the healthcare system. Robust and accurate analysis of medical data is crucial to achieving a successful diagnosis from physicians. Traditional diagnostic methods are highly time-consuming and prone to handmade errors. Cost is reduced and performance is improved by adopting computer-aided diagnosis methods. Usually, the performance of traditional machine learning (ML) classification methods much depends on both feature extraction and selection methods that are sensitive to colors, shapes, and sizes, which conveys a complex solution when facing classification tasks in medical imaging. Currently, deep learning (DL) tools have become an alternative solution to overcome the drawbacks of traditional methods that make use of handmade features. In this paper, a new DL approach based on a hybrid deep convolutional neural network model is proposed for the automatic classification of several different types of medical images. Specifically, gradient vanishing and over-fitting issues have been properly addressed in the proposed model in order to improve its robustness by means of different tested techniques involving residual links, global average pooling layers, dropout layers, and data augmentation. Additionally, we employed the idea of parallel convolutional layers with the aim of achieving better feature representation by adopting different filter sizes on the same input and then concatenated as a result. The proposed model is trained and tested on the ICIAR 2018 dataset to classify hematoxylin and eosin-stained breast biopsy images into four categories: invasive carcinoma, in situ carcinoma, benign tumors, and normal tissue. As the experimental results show, our proposed method outperforms several of the state-of-the-art methods by achieving rate values of 93.2% and 89.8% for both image- and patch-wise image classification tasks, respectively. Moreover, we fine-tuned our model to classify foot images into two classes in order to test its robustness by considering normal and abnormal diabetic foot ulcer (DFU) image datasets. In this case, the model achieved an F1 score value of 94.80% on the public DFU dataset and 97.3% on the private DFU dataset. Lastly, transfer learning (TL) has been adopted to validate the proposed model with multiple classes with the aim of classifying six different wound types. This approach significantly improves the accuracy rate from a rate of 76.92% when trained from scratch to 87.94% when TL was considered. Our proposed model has proven its suitability and robustness by addressing several medical imaging tasks dealing with complex and challenging scenarios.

如今，医学成像在诊断整个医疗系统的患者中的各种疾病中起着至关重要的作用。医学数据的鲁棒和准确的分析对于获得医生的成功诊断至关重要。传统的诊断方法非常耗时，容易出现人为错误。通过采用计算机辅助诊断方法，可以降低成本并提高性能。通常，传统机器学习（ML）分类方法的性能很大程度上取决于对颜色，形状和大小敏感的特征提取和选择方法，这在面对医学成像中的分类任务时传达了一个复杂的解决方案。当前，深度学习（DL）工具已成为替代解决方案，以克服利用手工功能的传统方法的弊端。本文提出了一种基于混合深度卷积神经网络模型的新的DL方法，用于对几种不同类型的医学图像进行自动分类。具体而言，在所提出的模型中已适当解决了梯度消失和过度拟合的问题，以便通过不同的测试技术（包括剩余链接，全局平均池化层，数据丢失层和数据扩充）来提高其鲁棒性。此外，我们采用了并行卷积层的思想，目的是通过在同一输入上采用不同的滤波器大小，然后进行级联来实现更好的特征表示。在ICIAR 2018数据集上对提出的模型进行了训练和测试，以将苏木精和曙红染色的乳腺活检图像分为四类：浸润性癌，原位癌，良性肿瘤和正常组织。如实验结果所示，我们提出的方法在实现图像分类和分片图像分类任务方面分别达到93.2％和89.8％的比率值，从而优于几种最新方法。此外，我们通过考虑正常和异常糖尿病足溃疡（DFU）图像数据集对模型进行了微调，以将足部图像分为两类，以测试其健壮性。在这种情况下，该模型在公共DFU数据集上的F1得分值为94.80％，在私有DFU数据集上达到97.3％。最后，采用转移学习（TL）来验证提议的具有多个类别的模型，目的是对六种不同的伤口类型进行分类。从零开始训练时，此方法将准确率从76.92％显着提高到87。当考虑TL时为94％。我们提出的模型通过解决一些应对复杂而富挑战性的情况的医学成像任务，证明了其适用性和鲁棒性。