A Proposed Framework of Transfer Learning-based Explainable Hybrid Model for Early Cervical Cancer Detection in Bangladeshi Women

The study proposes a transfer learning-based explainable hybrid framework for early cervical cancer detection for women in Bangladesh. The study is ongoing and plans to combine Bangladeshi Pap-smear data, CNN-based image features, and clinical risk factors. Various transfer learning models such as VGG, ResNet, InceptionV3, DenseNet and EfficientNet will be evaluated and SVM, Random Forest and XGBoost will be considered for classification.
In addition, it is planned to explain the decision of the model using Grad-CAM, SHAP and LIME and apply focal loss, SMOTE/ADASYN and GAN-based techniques to deal with class imbalance . Overall, the study aims to combine transfer learning, hybrid CNN-ML, multimodal XAI and Bangladeshi local data to create an interpretable and effective cervical cancer screening framework.