Deep Learning in Cervical Cancer Diagnosis: Architecture, Opportunities, and Open Research Challenges

Abstract

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

Meticulous Review: Cutting-Edge Cervix Cancer Stratification Using Image Processing And Machine Learning

“…Various important aspects of applying machine learning techniques for the healthcare field are portrayed [50] in figure 4. Managing the patients' records and medical histories, suggesting primary care treatments of chronic diseases, cancer screening, surveillance, tumor characterization, and drug discovery are the key line areas where machine learning deep learning techniques provide satisfactory outcomes in terms of prediction and detection [31], [26].…”

“…The cellular-level approach includes pap-smear, Human papillomaviruses-Deoxyribonucleic acid (HPV-DNA) testing, liquid based cytology (LBC) [9], [53], and electromagnetic spectroscopies [11]. The tissue-level screening technique includes VILI, or VIA [22], [24], hyperspectral diagnostic imaging (HSDI) [18], [20], [9], [25], colposcopy [26], cervicography, digital cervigrams, mobile phone images, pocket colposcopes. However, each of the techniques has its advantages and disadvantages, and all the techniques mentioned highly skilled experts for the judgment or prediction of the results.…”

“…Along with above mentioned issues, these techniques also have a lack of accuracy and information [13], [19]. Hence , computer assisted screening methods are being introduced to speed up and accurate the whole diagnostic process [26].…”

Cervical precancerous lesion and cancer detection on Pap smear image using machine learning algorithm

“…Artificial intelligence and deep learning assisted medical diagnosis able to efficiently and scientifically deal with a large clinical data and achieve comparable performance on various medical tasks [21]. Deep Learning based solution is an important methods to get outstanding performance in analyze and classification of cervical cancer [22]. Artificial Intelligence assisted and Deep Learning was implemented for diagnosis and classification of cervical lesions [23] Artificial intelligence (AI) has been increasingly applied in the diagnosis of various diseases, such as the classification of skin lesions or tumors [24], skin cancer using convolutional neural network [25], diagnosis and classification of retinal diseases [26], and imaging diagnosis of cancer [27], and has shown promising application value.…”

A Predictive Model to Detect Cervical Diseases Using Convolutional Neural Network Algorithms and Digital Colposcopy Images

“…Cancers and, most 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 accurately detect the early stages of cervical diseases due to the limitations and the type of medical detection tests used in those solutions [8]. This study proposes a predictive model using DL algorithms to detect different classes of cervical diseases, including their early stages, and explores the impacts of increasing the number of classes on the accuracy of the proposed model.…”