Abstract:
Cervical cancer is the most common cancer and the leading cause of cancer mortality among females
worldwide. It is the second most common and second most deadly cancer in Ethiopia. The growth
rate of cervical cancer in Ethiopia is 16.48%. This mainly happened due to the lack of efficient cancer
disease prediction and the absence of a system that would determine the risk level. In addition,
modern diagnostic instrumentation usually requires sophisticated infrastructure, stable electrical
power, expensive reagents, long examine times, and highly trained personnel which are not often
available in limited resource settings. Survival from cervical cancer can be increased through
advances in screening methods and early diagnosis. Patient evaluation, clinical examination,
screening, and pathological assessment (biopsy test) are common methods of cervical cancer
diagnosis. Among these, pathological assessment has the potential in identifying cervical cancer.
However, current diagnosis using biopsy tests is commonly done through visual inspection. This
manual diagnosis is time-consuming, tedious, and subjective which may lead to misdiagnosing.
Recently, machine learning and deep learning methods have gained popularity to automate the
manual cervical cancer diagnosis mechanism and have shown promising results. In low resource
setting, utilizing the historical patient card sheet for predictive evidence-based risk analysis is
overlooked and underrated, as well as challenging to use at the point of care since it is not fully
automated. To fill this gap, an integrated approach was employed, combining two models which are
predictive risk analysis and biopsy detection model to develop a comprehensive multimodal model
for analyzing risk and detecting cervical cancer. The result of a multimodal cervical cancer risk
analysis and detection model, designed for low-resource settings. The microscopic images and
patient card data were collected from Jimma Medical Center (JMC). In preprocessing steps all image
paths through the process of image enhancement using CLAHE algorithm, data augmentation, and
feature selection were applied to enhance the quality of both image and text datasets. The research
developed four distinct classification models, with a specific focus on tuning the hyperparameters of
ResNet50 and VGG16 models for image data analysis. The VGG16 model excelled in detecting
cervical cancer from images, showed a test accuracy of 91%, while the text-based data were
effectively analyzed using a random forest model, attaining a 96.2% test accuracy. Finally, applied
multimodal learning approach and integrated the model that have better accuracy using average
probability, the test accuracy showed that the proposed method is 92% accurate.
