Özdaş Mehmet Batuhan, Uysal Fatih, Hardalaç Fırat
Department of Electrical and Electronics Engineering, Faculty of Engineering, Gazi University, Ankara TR 06570, Turkey.
Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, Kafkas University, Kars TR 36100, Turkey.
Diagnostics (Basel). 2023 Jan 25;13(3):433. doi: 10.3390/diagnostics13030433.
In recent years, the number of studies for the automatic diagnosis of biomedical diseases has increased. Many of these studies have used Deep Learning, which gives extremely good results but requires a vast amount of data and computing load. If the processor is of insufficient quality, this takes time and places an excessive load on the processor. On the other hand, Machine Learning is faster than Deep Learning and does not have a much-needed computing load, but it does not provide as high an accuracy value as Deep Learning. Therefore, our goal is to develop a hybrid system that provides a high accuracy value, while requiring a smaller computing load and less time to diagnose biomedical diseases such as the retinal diseases we chose for this study. For this purpose, first, retinal layer extraction was conducted through image preprocessing. Then, traditional feature extractors were combined with pre-trained Deep Learning feature extractors. To select the best features, we used the Firefly algorithm. In the end, multiple binary classifications were conducted instead of multiclass classification with Machine Learning classifiers. Two public datasets were used in this study. The first dataset had a mean accuracy of 0.957, and the second dataset had a mean accuracy of 0.954.
近年来，用于生物医学疾病自动诊断的研究数量有所增加。其中许多研究都采用了深度学习，它能给出极其出色的结果，但需要大量数据和计算量。如果处理器质量不足，这会耗费时间并给处理器带来过大负载。另一方面，机器学习比深度学习速度更快，且没有过多的计算量需求，但它提供的准确率不如深度学习高。因此，我们的目标是开发一种混合系统，该系统在诊断诸如我们在本研究中选择的视网膜疾病等生物医学疾病时，既能提供高准确率，又需要较小的计算量和更短的时间。为此，首先通过图像预处理进行视网膜层提取。然后，将传统特征提取器与预训练的深度学习特征提取器相结合。为了选择最佳特征，我们使用了萤火虫算法。最后，使用机器学习分类器进行多个二分类，而不是多分类。本研究使用了两个公共数据集。第一个数据集的平均准确率为0.957，第二个数据集的平均准确率为0.954。
