Nowadays, deep learning (DL) is a popular tool used in various applications in different fields, including the medical domain. DL techniques can cope with several challenges, which are difficult to resolve via traditional artificial intelligence (AI) techniques. Cervical cancer (CC) is one of the leading reasons for death in females and ranks second after breast cancer, with more than 700 mortalities daily. This number is estimated to be 400,000 annually by 2030. However, if the cancer is detected in the early and precancerous stages, it is completely curable. Pap smear and colposcopy are the most widely used screening methods for the detection of cervical cancer. But manual screening approach suffers from a high false rate due to human errors. To overcome this challenge, machine learning (ML) and DL-based computer-aided diagnostic (CAD) techniques are being extensively expanded to automatically segment and categorize cervical cytology and colposcopy images. These methods increase the accuracy of detecting different stages of cervical cancer. Hence, there is an increased interest in creating computer-aided solutions for CC screening, especially in less-developed countries where the majority of cervical cancer-related fatalities occur. This review overviews state-of-the-art approaches that use DL techniques to analyze cervical cytology and screening images. It reviews and discusses relevant DL techniques, their architectures, classification methods, and the segmentation of cervical cytology and colposcopy images. Finally, it reviews the DL algorithms that are currently used in CC screening and offers useful insights, research opportunities and future directions in this field

Cervical diseases, specifically cervical cancer (CC), are among the leading causes of death around the globe, imposing a significant challenge to scientists and healthcare providers dealing with cervical disease patients. None of the existing solutions can detect various cervical diseases, which would lead the experts to accurately detect the early stages of cervical diseases due to the equipment limitations and the type of medical detection tests used in those solutions. New technologies have been developed to enable more rapid and sensitive cervical cancer screening using deep learning algorithms. This study proposes a predictive model using deep learning (DL) algorithms and colposcopy images to detect different classes of cervical diseases, including different stages of cervical diseases. This offers the medical sector an opportunity for early-stage diagnosis of cervical diseases. Four rounds of experiments were conducted in this research to evaluate the performance of the proposed model. According to the results, the proposed model can detect classes (stages) of cervical diseases while it obtains high accuracy. The rate of accuracy in the training stage was above 92%, and the highest achieved accuracy was 99% in the third experiment. Also, in this round of the experiment, the model could achieve the highest performance results in accuracy, and sensitivity with values of 98% and 98%, respectively. Notably, the third and last experiments achieved a perfect specificity value of 1.