Optimizing cervical cancer diagnosis with a hybrid deep neural network and progressive resizing on pap smear WSIs

Nowadays, computer-aided diagnostic (CAD) systems powered by artificial intelligence (AI) are becoming increasingly prevalent in cervical cancer diagnosis. Automatic selection of features by the deep convolutional neural networks (CNN) is a more prominent substitute than the conventional machine learning (ML) models, as they require handcrafted cell segmentation and extraction. Pre-trained deep models using transfer learning and fine-tuning make these models faster and more efficient, even with the limited data availability. This article proposes a novel hybrid deep network with progressive resizing (HDNPR) for classifying whole slide images (WSI) of pap smear slides. This model trains fine-tuned deep learning (DL) models on pre-trained weights utilizing progressively resized and augmented training data of size 224 × 224, 512 × 512, and 1024 × 1024 pixels, laid over with transfer learning. The hybrid deep features produced by the concatenation of extracted features from two prevalent fine-tuned deep learning networks, VGG-16 and ResNet-152, are applied to the fully connected network (FCN) stage for detecting different classes of cervical cancer. The proposed HDNPR network is evaluated for both multiclass and binary classification, and it is able to attain an accuracy score of 97.45% for 5-class classification and 98.58% for 2-class classification, respectively.