Advancement in Deep Learning Methods for Diagnosis and Prognosis of Cervical Cancer

Abstract

Cervical cancer is the leading cause of death in women, mainly in developing countries, including India. Recent advancements in technologies could allow for more rapid, cost-effective, and sensitive screening and treatment measures for cervical cancer. To this end, deep learning-based methods have received importance for classifying cervical cancer patients into different risk groups. Furthermore, deep learning models are now available to study the progression and treatment of cancerous cervical conditions. Undoubtedly, deep learning methods can enhance our knowledge toward a better understanding of cervical cancer progression. However, it is essential to thoroughly validate the deep learning-based models before they can be implicated in everyday clinical practice. This work reviews recent development in deep learning approaches employed in cervical cancer diagnosis and prognosis. Further, we provide an overview of recent methods and databases leveraging these new approaches for cervical cancer risk prediction and patient outcomes. Finally, we conclude the state-of-the-art approaches for future research opportunities in this domain.

Keywords: Deep learning; cervical cancer; diagnosis; neural networks; risk prediction; sensitive screening.

Fig. (1)

Various types of techniques used for the prediction of cancer. Artificial Intelligence is widely used in the prognosis of cancer. These four techniques are supervised learning methods that are most commonly used to detect different types of cancer with high accuracy.

Fig. (2)

Relation between deep learning and machine learning techniques. In this image, artificial intelligence can be considered as the universal set with subsets - machine learning followed by neural networks and, lastly, by deep learning. Deep learning is a small niche in machine learning and is a highly specialized technique used to solve complex problems.

Fig. (3)

Performance of deep learning vs. traditional algorithms. As seen in the image, deep learning algorithms outperform traditional algorithms as the dataset becomes larger. Deep learning is hence preferred in cancer research as the amount of data is tremendous, and accuracy is to be maximized.

Fig. (4)

A simple representation of a deep neural network. A deep neural network is composed of several hidden layers as compared to a simple network. These hidden layers are mathematical functions that convert the input into the desired output, which is a continuous process as the data passes through each layer.

Fig. (5)

A simple representation of a convolutional neural network. A CNN uses convolution layers and pooling layers. A convolution is essentially a filter that produces the desired data for the next layer. In other words, it executes a specific function on the data to produce a specific output. The pooling layer enhances the degree of convolutions and acts as an aggregator. It also reduces noise and helps in extracting the required features.

Fig. (6)

A simple representation of a recurrent neural network.

Fig. (7)

Principle of a recurrent neural network. The data is continuously refed into the neural network, and this leads to increased learning and higher accuracy.

Fig. (8)

Pipeline for prediction of cervical cancer using Deep Learning. This is a simple pipeline that utilizes deep learning on image-based datasets (of the cervix) and detects anomalies (tumors). This is a complete pipeline that can be used to train models and detect malignant and benign tumors pertaining to cervical cancer. The DNN training can be specialized as per the requirement, and RNNs, CNNs, R-CNNs, LSTMS, etc., can be used for prediction.