PDF(Pubmed)
Abstract：
Interpretation of medical images with a computer-aided diagnosis (CAD) system is arduous because of the complex structure of cancerous lesions in different imaging modalities, high degree of resemblance between inter-classes, presence of dissimilar characteristics in intra-classes, scarcity of medical data, and presence of artifacts and noises. In this study, these challenges are addressed by developing a shallow convolutional neural network (CNN) model with optimal configuration performing ablation study by altering layer structure and hyper-parameters and utilizing a suitable augmentation technique. Eight medical datasets with different modalities are investigated where the proposed model, named MNet-10, with low computational complexity is able to yield optimal performance across all datasets. The impact of photometric and geometric augmentation techniques on different datasets is also evaluated. We selected the mammogram dataset to proceed with the ablation study for being one of the most challenging imaging modalities. Before generating the model, the dataset is augmented using the two approaches. A base CNN model is constructed first and applied to both the augmented and non-augmented mammogram datasets where the highest accuracy is obtained with the photometric dataset. Therefore, the architecture and hyper-parameters of the model are determined by performing an ablation study on the base model using the mammogram photometric dataset. Afterward, the robustness of the network and the impact of different augmentation techniques are assessed by training the model with the rest of the seven datasets. We obtain a test accuracy of 97.34% on the mammogram, 98.43% on the skin cancer, 99.54% on the brain tumor magnetic resonance imaging (MRI), 97.29% on the COVID chest X-ray, 96.31% on the tympanic membrane, 99.82% on the chest computed tomography (CT) scan, and 98.75% on the breast cancer ultrasound datasets by photometric augmentation and 96.76% on the breast cancer microscopic biopsy dataset by geometric augmentation. Moreover, some elastic deformation augmentation methods are explored with the proposed model using all the datasets to evaluate their effectiveness. Finally, VGG16, InceptionV3, and ResNet50 were trained on the best-performing augmented datasets, and their performance consistency was compared with that of the MNet-10 model. The findings may aid future researchers in medical data analysis involving ablation studies and augmentation techniques.
摘要：
由于在不同的成像方式下癌性病变的复杂结构，使用计算机辅助诊断（CAD）系统对医学图像进行解释是艰巨的，班级之间高度相似，类内存在不同的特征，医疗数据的稀缺性，以及工件和噪音的存在。在这项研究中,这些挑战通过开发具有最佳配置的浅层卷积神经网络(CNN)模型来解决,该模型通过改变层结构和超参数并利用合适的增强技术来执行消融研究。研究了八个具有不同模态的医疗数据集，其中所提出的模型，名为MNet-10，具有低计算复杂度，能够在所有数据集上产生最佳性能。还评估了光度和几何增强技术对不同数据集的影响。我们选择乳房X线照片数据集作为最具挑战性的成像方式之一进行消融研究。在生成模型之前，使用这两种方法来增强数据集。首先构建基本CNN模型,并将其应用于增强和非增强乳房X线照片数据集,其中利用光度数据集获得最高精度。因此,通过使用乳房X线照片光度数据集对基础模型进行消融研究来确定模型的架构和超参数。之后,网络的健壮性和不同增强技术的影响是通过用其余的七个数据集训练模型来评估的。我们在乳房X线照片上获得了97.34%的测试准确率，98.43%的人患皮肤癌,99.54％的脑肿瘤磁共振成像（MRI），97.29%的COVID胸部X光检查，96.31%在鼓膜上,胸部计算机断层扫描（CT）扫描占99.82％，和98.75％的乳腺癌超声数据集通过光度增强和96.76％的乳腺癌显微活检数据集通过几何增强。此外，使用所提出的模型，使用所有数据集来探索一些弹性变形增强方法，以评估其有效性。最后,VGG16、InceptionV3和ResNet50在性能最佳的增强数据集上进行了训练，并将它们的性能一致性与MNet-10模型进行了比较。这些发现可能会帮助未来的研究人员进行涉及消融研究和增强技术的医疗数据分析。
