Early Detection and Categorization of Cervical Cancer Cells Using Smoothing Cross Entropy-Based Multi-Deep Transfer Learning

Cervical cancer is one of the leading causes of death in women worldwide. Prompt and accurate diagnosis is imperative for the treatment of cervical cancer through the utilization of pap smear slides, albeit it is a multifaceted and time-intensive process. An automatic diagnosis model based on deep learning models, particularly a convolutional neural network (CNN), can enhance cervical cancer's accuracy and rapid identification. This paper proposes a cross entropy-based multi-deep transfer learning model for the early detection and categorization of cervical cancer cells. The proposed model consists of four phases: the pre-processing phase, the feature extraction and fusion phase, the feature reduction phase, and the feature classification phase. In the pre-processing phase, cervical cancer input images are resized to $64\times 64$ to match the input layer of the deep neural network. Feature extraction and fusion phase are adapted to extract features through different deep transfer learning models, including MobileNet, DenseNet, EfficientNet, Xception, RegNet, and ResNet-50, followed by a fusion process for all extracted features. As for the feature reduction process phase, Principal Component Analysis (PCA) is applied as a feature reduction technique. Finally, a pipeline of three dense layers completes the classification process. A novel loss function termed smoothing cross-entropy is presented to enhance classification performance. The performance of the proposed model is validated using benchmark datasets, namely the SIPaKMeD dataset. According to the results, the suggested model attains a remarkable accuracy of 97% for the SIPaKMeD datasets using 676 features.