Improved and Explainable Cervical Cancer Classification using Ensemble Pooling of Block Fused Descriptors

Abstract: Cervical cancer is the second most common cancer in women and causes high death rates. Earlier models for detecting cervical cancer had limited success. In this work, we propose new models that substantially outperform previous models. Previous studies show that pretrained ResNets extract features from cervical cancer images well. Hence, our first model involves working with three ResNets (50, 101, 152). All the existing works use only the last convolution block of their respective ResNet, which captures abstract features (e.g., shapes, objects). However, we believe that detailed features (e.g., color, edges, texture), coming from earlier convolution blocks, are equally important for cancer (specifically cervical cancer) classification. Since now the number of features become large, we use a novel feature selection technique of Global Max Pooling for detailed features and Global Average Pooling for abstract features. Hence, our second model consists of the resulting Cascaded Block Fused variants of the three ResNets. To improve the performance further, we combine and normalize the features of the three standard ResNets as well as our proposed three Cascaded Block Fused ResNets. This type of combination is also new in cancer classification domain (also in cervical cancer), and results in our third and fourth models, respectively. We use a linear SVM for classification. We exhaustively perform experiments on two public datasets, IARC and AnnoCerv, achieving an average performance of 97.92% and 92.97% surpassing standard ResNets performance of 90.89% and 87.97%, respectively. We outperform the competitive approach available on IARC dataset with an average gain of 13.20%, while no prior competitive work available on AnnoCerv. Additionally, we introduce a novel SHAP+LIME explainability method, accurately identifying the cancerous region in 97% of cases.