Mendeley Computer Vision Project

While a publicly available benchmark dataset provides a base for the development of new algorithms and comparison of results, hospital-based data collected from the real-world clinical setup is also very important in automated AI-based medical research likewise in disease diagnosis or categorization of predicted disease for tissue level staging or any class identification as per standard protocol so that the developed algorithm works with as much accuracy as possible in the regional context. The repository supports research work related to image segmentation and final classification for a complete decision support system. Liquid based cytology is one of the cervical screening tests. The repository consists of total 963 images sub-divided into four sets of images representing the four classes of pre-cancerous and cancerous lesions of cervical cancer as per standards under The Bethesda System. The pap smear images were captured in 40x magnification using Leica ICC50 HD microscope which is collected and prepared using the liquid-based cytology technique from 460 patients. Microscopic investigation of abnormal changes in cell-level enables detection of malignancy or pre-malignant characteristics. This procedure is time-consuming and subject to inter or intra-observer variability which is why computer-assisted diagnosis can improve the overall disease diagnosis time period to proceed with rapid treatment and therapy which can limit late diagnosis of cervical cancer.

Cite the original dataset: Hussain, Elima (2019), “Liquid based cytology pap smear images for multi-class diagnosis of cervical cancer”, Mendeley Data, V4, doi: 10.17632/zddtpgzv63.4

Annotation extracted from: https://universe.roboflow.com/projet2/mendeley-dataset-0cl0l