We introduce a machine learning-based method for fully automated diagnosis of sickle cell disease of poor-quality unstained images of a mobile microscope. Our method is capable of distinguishing between diseased, trait (carrier), and normal samples unlike the previous methods that are limited to distinguishing the normal from the abnormal samples only. The novelty of this method comes from distinguishing the trait and the diseased samples from challenging images that have been captured directly in the field. The proposed approach contains two parts, the segmentation part followed by the classification part. We use a random forest algorithm to segment such challenging images acquitted through a mobile phone-based microscope. Then, we train two classifiers based on a random forest (RF) and a support vector machine (SVM) for classification. The results show superior performances of both of the classifiers not only for images which have been captured in the lab, but also for the ones which have been acquired in the field itself.
翻译:我们采用了一种基于机器的学习法,对移动显微镜中质量差的、不光彩的图像的镰状细胞疾病进行完全自动诊断。我们的方法能够区分疾病、性格(载体)和正常样本,而以前的方法仅限于区分正常和异常样本。这种方法的新颖之处在于将特性和疾病样本与直接实地采集的具有挑战性的图像区分开来。拟议方法包含两个部分,即分解部分,然后是分类部分。我们使用随机森林算法对通过移动电话显微镜解析出的挑战性图像进行分解。然后,我们用随机森林(RF)和辅助矢量机(SVM)来培训两个基于随机森林(SVM)的分类器进行分类。结果显示,分类器的优异性不仅表现在实验室中采集的图像,而且还表现在野外获取的图像上。