  Recently, Computer-Aided Diagnosis (CAD) systems have emerged as
indispensable tools in clinical diagnostic workflows, significantly alleviating
the burden on radiologists. Nevertheless, despite their integration into
clinical settings, CAD systems encounter limitations. Specifically, while CAD
systems can achieve high performance in the detection of lung nodules, they
face challenges in accurately predicting multiple cancer types. This limitation
can be attributed to the scarcity of publicly available datasets annotated with
expert-level cancer type information. This research aims to bridge this gap by
providing publicly accessible datasets and reliable tools for medical
diagnosis, facilitating a finer categorization of different types of lung
diseases so as to offer precise treatment recommendations. To achieve this
objective, we curated a diverse dataset of lung Computed Tomography (CT)
images, comprising 330 annotated nodules (nodules are labeled as bounding
boxes) from 95 distinct patients. The quality of the dataset was evaluated
using a variety of classical classification and detection models, and these
promising results demonstrate that the dataset has a feasible application and
further facilitate intelligent auxiliary diagnosis.
