This work proposes reinforcement learning for correctly identifying pneumonia and tuberculosis (TB) using a repository of X ray images. To our knowledge, this is a first attempt at employing reinforcement learning for pneumonia and TB classification. In particular, modified fuzzy Q learning (MFQL) algorithm in conjunction with wavelet based pre-processing has been used to build a classifier for identifying pneumonia and tuberculosis's severity. Proposed classifier is a self-learning one and uses pneumonia dataset (no pneumonia, mild pneumonia and severe pneumonia) and tuberculosis dataset (TB present, TB absent) samples to classify X ray images of subjects. Results indicate that MFQL based approach achieves high accuracy and fares much better over contemporary Support Vector Machine (SVM) and k-Nearest Neighbor (KNN) classifiers. Proposed classifier can be a useful tool for pneumonia and tuberculosis diagnosis in a practical setting.

这项工作提出了使用 X 射线图像库正确识别肺炎和结核病 (TB) 的强化学习。据我们所知，这是首次尝试将强化学习用于肺炎和结核病分类。特别是，改进的模糊 Q 学习 (MFQL) 算法与基于小波的预处理相结合，已被用于构建用于识别肺炎和结核病严重程度的分类器。提出的分类器是一种自学习分类器，使用肺炎数据集（无肺炎、轻度肺炎和重症肺炎）和结核病数据集（存在结核病，不存在结核病）样本对受试者的 X 射线图像进行分类。结果表明，基于 MFQL 的方法比当代支持向量机 (SVM) 和 k-最近邻 (KNN) 分类器具有更高的准确性和性能。