Given a short query with keyphrases about a research idea in 2017 in the domain of artificial intelligence, recommend the relevant datasets to validate the research idea from options provided. A short description of the datasets is also provided to help you.

Query: We propose an automated method for detecting the optic disc from retinal images. Our method first detects the

Keyphrases: optic disc detection retinal image

Dataset description:
G1020: A large publicly available retinal fundus image dataset for glaucoma classification called G1020. The dataset is curated by conforming to standard practices in routine ophthalmology and it is expected to serve as standard benchmark dataset for glaucoma detection.
ACDC: The goal of the Automated Cardiac Diagnosis Challenge (ACDC) challenge is to:   compare the performance of automatic methods on the segmentation of the left ventricular endocardium and epicardium as the right ventricular endocardium for both end diastolic and end systolic phase instances; compare the performance of automatic methods for the classification of the examinations in five classes (normal case, heart failure with infarction, dilated cardiomyopathy, hypertrophic cardiomyopathy, abnormal right ventricle). The overall ACDC dataset was created from real clinical exams acquired at the University Hospital of Dijon.
EyeQ: Dataset with 28,792 retinal images from the EyePACS dataset, based on a three-level quality grading system (i.e., Good',Usable' and `Reject') for evaluating RIQA methods.
DRIVE: The Digital Retinal Images for Vessel Extraction (DRIVE) dataset is a dataset for retinal vessel segmentation. It consists of a total of JPEG 40 color fundus images; including 7 abnormal pathology cases.

Options:- A: G1020  B: ACDC  C: EyeQ  D: DRIVE
DRIVE