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Cervical cancer is the fourth most common category of cancer, affecting morethan 500,000 women annually, owing to the slow detection procedure. Earlydiagnosis can help in treating and even curing cancer, but the tedious,time-consuming testing process makes it impossible to conduct population-wisescreening. To aid the pathologists in efficient and reliable detection, in thispaper, we propose a fully automated computer-aided diagnosis tool forclassifying single-cell and slide images of cervical cancer. The main concernin developing an automatic detection tool for biomedical image classificationis the low availability of publicly accessible data. Ensemble Learning is apopular approach for image classification, but simplistic approaches thatleverage pre-determined weights to classifiers fail to perform satisfactorily.In this research, we use the Sugeno Fuzzy Integral to ensemble the decisionscores from three popular pretrained deep learning models, namely, Inceptionv3, DenseNet-161 and ResNet-34. The proposed Fuzzy fusion is capable of takinginto consideration the confidence scores of the classifiers for each sample,and thus adaptively changing the importance given to each classifier, capturingthe complementary information supplied by each, thus leading to superiorclassification performance. We evaluated the proposed method on three publiclyavailable datasets, the Mendeley Liquid Based Cytology (LBC) dataset, theSIPaKMeD Whole Slide Image (WSI) dataset, and the SIPaKMeD Single Cell Image(SCI) dataset, and the results thus yielded are promising. Analysis of theapproach using GradCAM-based visual representations and statistical tests, andcomparison of the method with existing and baseline models in literaturejustify the efficacy of the approach.

Ensemble of CNN classifiers using Sugeno Fuzzy Integral Technique for Cervical Cytology Image Classification