The paper presents a method of a Convolutional Neural Networks (CNN) model for image classification with image preprocessing and hyperparameters tuning, aiming at increasing the predictive performance for COVID-19 diagnosis while avoiding deeper and thus more complex alternatives. Firstly, the CNN model includes four similar convolutional layers followed by a flattening and two dense layers. This work proposes a less complex solution based on simply classifying 2D slices of CT scans using a CNN model. Despite the simplicity in architecture, the proposed CNN model showed improved quantitative results exceeding state-of-the-art on the dataset of images, in terms of the macro F1 score. The results were achieved on the original CT slices of the dataset. Secondly, the original dataset was processed via anatomy-relevant masking of slice, removing none-representative slices from the CT volume, and hyperparameters tuning. For slice processing, a fixed-sized rectangular area was used for cropping an anatomy-relevant region-of-interest in the images, and a threshold based on the number of white pixels in binarized slices was employed to remove none-representative slices from the 3D-CT scans. The CNN model with a learning rate schedule and an exponential decay and slice flipping techniques was deployed on the processed slices. The proposed method was used to make predictions on the 2D slices. For final diagnosis at patient level, majority voting was applied on the slices of each CT scan to take the diagnosis. The macro F1 score of the proposed method well-exceeded the baseline approach and other alternatives on the validation set as well as on a test partition of previously unseen images from COV-19CT-DB dataset partitions.

翻译：本文展示了以图像预处理和超参数调控图像前处理图像和超光度参数图像分类模型(CNN)的图像分类方法,目的是提高COVID-19诊断的预测性能,同时避免更深、更复杂的替代品。首先,CNN模型包括四个相似的卷层,随后是平坦和两个稠密的层。这项工作提出了一个较不复杂的解决方案,其依据是使用CNN模型对2D切片的CT扫描进行简单分类。尽管结构简单,但拟议的CNN模型显示,就宏观F1评分而言,在图像数据集的诊断性能方面,质量结果超过了最新的CT诊断性结果。在数据集原CT切片的CT切片上取得了结果。第二,原始数据集通过切片的解剖面遮罩处理,从CT音量中去除无代表的切片片片片片片片,对切片处理使用固定规模的矩形区域进行裁剪切,在图像的解剖面区域中,根据白分的直径直径直径的直径直径直径直径直径直径直径直径直径直径直径,在2号直径直径直径直径直径路路路路路路路路路路路路路路,在S压法上,在Sir平平路路路路路路路路路路路路路路路路路路路,在S。