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

• URL: http://arxiv.org/abs/2108.09460v1
• Date: Sat, 21 Aug 2021 08:41:41 GMT
• Title: Ensemble of CNN classifiers using Sugeno Fuzzy Integral Technique for Cervical Cytology Image Classification
• Authors: Rohit Kundu, Hritam Basak, Akhil Koilada, Soham Chattopadhyay, Sukanta Chakraborty, Nibaran Das
• Abstract summary: We propose a fully automated computer-aided diagnosis tool for classifying single-cell and slide images of cervical cancer. We use the Sugeno Fuzzy Integral to ensemble the decision scores from three popular deep learning models, namely, Inception v3, DenseNet-161 and ResNet-34.
• Score: 1.6986898305640261
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cervical cancer is the fourth most common category of cancer, affecting more than 500,000 women annually, owing to the slow detection procedure. Early diagnosis can help in treating and even curing cancer, but the tedious, time-consuming testing process makes it impossible to conduct population-wise screening. To aid the pathologists in efficient and reliable detection, in this paper, we propose a fully automated computer-aided diagnosis tool for classifying single-cell and slide images of cervical cancer. The main concern in developing an automatic detection tool for biomedical image classification is the low availability of publicly accessible data. Ensemble Learning is a popular approach for image classification, but simplistic approaches that leverage pre-determined weights to classifiers fail to perform satisfactorily. In this research, we use the Sugeno Fuzzy Integral to ensemble the decision scores from three popular pretrained deep learning models, namely, Inception v3, DenseNet-161 and ResNet-34. The proposed Fuzzy fusion is capable of taking into consideration the confidence scores of the classifiers for each sample, and thus adaptively changing the importance given to each classifier, capturing the complementary information supplied by each, thus leading to superior classification performance. We evaluated the proposed method on three publicly available datasets, the Mendeley Liquid Based Cytology (LBC) dataset, the SIPaKMeD Whole Slide Image (WSI) dataset, and the SIPaKMeD Single Cell Image (SCI) dataset, and the results thus yielded are promising. Analysis of the approach using GradCAM-based visual representations and statistical tests, and comparison of the method with existing and baseline models in literature justify the efficacy of the approach.

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