Dataset Information

Liquid based-cytology Pap smear dataset for automated multi-class diagnosis of pre-cancerous and cervical cancer lesions.

ABSTRACT: 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 AI-based medical research for automated disease diagnosis, prediction or classifications as per standard protocol. Primary data must be constantly updated so that the developed algorithms achieve as much accuracy as possible in the regional context. This dataset would support research work related to image segmentation and final classification for a complete decision support system (https://doi.org/10.1016/j.tice.2020.101347) [1]. Liquid-based cytology (LBC) is one of the cervical screening tests. The repository consists of a total of 963 LBC images sub-divided into four sets representing the four classes: NILM, LSIL, HSIL, and SCC. It comprises pre-cancerous and cancerous lesions related to cervical cancer as per standards under The Bethesda System (TBS). The images were captured in 40x magnification using Leica ICC50 HD microscope collected with due consent from 460 patients visiting the O&G department of the public hospital with various gynaecological problems. The images were then viewed and categorized by experts of the pathology department.

SUBMITTER: Hussain E

PROVIDER: S-EPMC7186519 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Liquid based-cytology Pap smear dataset for automated multi-class diagnosis of pre-cancerous and cervical cancer lesions.

Hussain Elima E Mahanta Lipi B LB Borah Himakshi H Das Chandana Ray CR

Data in brief 20200422

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 AI-based medical research for automated disease diagnosis, prediction or classifications as per standard protocol. Primary data must be constantly updated so that the developed algorithms achieve as much accuracy as possible in the regional context. This dataset would support resear ...[more]

PMID: 32368601

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Project description:Cervical intraepithelial neoplasia (CIN) and cervical cancer are major health problems faced by women worldwide. The conventional Papanicolaou (Pap) smear analysis is an effective method to diagnose cervical pre-malignant and malignant conditions by analyzing swab images. Various computer vision techniques can be explored to identify potential precancerous and cancerous lesions by analyzing the Pap smear image. The majority of existing work cover binary classification approaches using various classifiers and Convolution Neural Networks. However, they suffer from inherent challenges for minute feature extraction and precise classification. We propose a novel methodology to carry out the multiclass classification of cervical cells from Whole Slide Images (WSI) with optimum feature extraction. The actualization of Conv Net with Transfer Learning technique substantiates meaningful Metamorphic Diagnosis of neoplastic and pre-neoplastic lesions. As the Progressive Resizing technique (an advanced method for training ConvNet) incorporates prior knowledge of the feature hierarchy and can reuse old computations while learning new ones, the model can carry forward the extracted morphological cell features to subsequent Neural Network layers iteratively for elusive learning. The Progressive Resizing technique superimposition in consultation with the Transfer Learning technique while training the Conv Net models has shown a substantial performance increase. The proposed binary and multiclass classification methodology succored in achieving benchmark scores on the Herlev Dataset. We achieved singular multiclass classification scores for WSI images of the SIPaKMed dataset, that is, accuracy (99.70%), precision (99.70%), recall (99.72%), F-Beta (99.63%), and Kappa scores (99.31%), which supersede the scores obtained through principal methodologies. GradCam based feature interpretation extends enhanced assimilation of the generated results, highlighting the pre-malignant and malignant lesions by visual localization in the images.

Dataset Information

Liquid based-cytology Pap smear dataset for automated multi-class diagnosis of pre-cancerous and cervical cancer lesions.

Publications

Liquid based-cytology Pap smear dataset for automated multi-class diagnosis of pre-cancerous and cervical cancer lesions.

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