CervixMed: Detecting cervical cancer based on combinational data using hybrid architecture

Cervical cancer is women's second most frequent malignancy globally, with a 60% mortality rate. Early identification through routine checkups is crucial because cervical cancer has a long latent period and begins with no overt signs. One of the most evident barriers to not having an early detection is the time required for the diagnostic process, which also accounts for indifference regarding its automation. These uncertainties occur for several reasons, while one of the most prominent causes is providing the report by the computer-aided diagnostic process only based on the patient's information in tabular form or only predicting the patient's cervix image. In this study, we proposed three concepts 1. Hybrid Architecture, 2. CervixMed, a novel CNN-based architecture, and 3. GUI (Graphical User Interface). In Hybrid Architecture, we propose a methodology that takes both types of data, i.e., tabular and image, and then merges the prediction provided by the models and shows the final output. For tabular data classification, we use traditional-machine learning models like KNN, SVM, LR, and RF, where LR and SVM provided the best accuracy of 99%. On the other hand, the image data is classified into one of the five categories using our novel lightweight CNN-based architecture, CervixMed, that outperforms other pre-trained models like DenseNet121, DenseNet169, DenseNet201, ResNetl52v2, and VGG19 by 94% in accuracy and 98% in sensitivity. The hybrid architecture combines the abovementioned models and chooses the best pair regarding accuracy. In this study, we demonstrated that combining LR and SVM with CervixMed outperforms the other combinations by 96.5% accuracy. Lastly, we demonstrated the methodology of the proposed GUI for interacting with the end-users with this hybrid model.