8.1 Introduction

Cancer is a disorder where the body's cells grow rapidly creating a malignant tumor; cancers are called after the body part where they begin or continue to spread to the nearest body part (e.g., lung cancer). Cancer found in the cervix tissue is generally known as cervical cancer. The cervix act as an interface between the uterus and vagina [1].

Cervical intraepithelial neoplasia (CIN) is the unwanted growth of a woman's cervix. It makes the way to the cancer called cervical cancer, and this precancerous CIN can be divided into three grades (CIN‐I, ‐II, and ‐III).

At the early stage of cervical cancer, no symptoms are commonly detected. Later signs can include frequent menstrual bleeding, stomach discomfort, or pain during periods or after menopause or sexual activity [1]. After sexual intercourse, pain is not a symptom, but postcoital bleeding can be a symptom of cervical cancer about to happen or present [2].

The main cause of cervical cancer is Human Papillomavirus infection (HPV). About 90% or cervical cancer cases have been confirmed to have been infected with HPV for a long period [3, 4]. Generally, HPV has moved from one human to another during sex. Thus, according to the Cleveland Clinic, about 80% of people who are sexually active will carry HPV at any point in their lives, but it doesn't mean that every woman infected with HPV will be diagnosed with cervical cancer. [5, 6]. The risk of cervical cancer increases with alcohol consumption and smoking (which are important risk factors in all cancers), use of birth control pills for a long time (five or more years), and having multiple sexual partners at an early age. However, these are less significant risk factors [1, 7]. Cervical cancer is diagnosed in women above 30, and cancer cells require about 10–20 years to develop [6].

Figure 8.1 depicts different cancer cases found, based on a 2018 report. Lung, breast, colon, and cervix are the top four. Lung cancer cases reported worldwide are almost 2.1 million with 85% mortality, and it stands on the top of the table. Breast and colon cancers reported on 2018 are 2 million and 1.1 million cases, respectively. Mortality of breast and colon cancer are almost 30% and 50%, respectively. Cervical cancer is the world's fourth most common cancer [8, 9] and the fourth most prominent in women [10]. Nearly 529 000 patients were diagnosed in 2012, which is nearly 8% of the estimated cancer cases [6]; its mortality rate is 50.4%.

Figure 8.2 depicts the cervical cancer cases and deaths of each country based on the Human Development Index (HDI). HDI is defined by education, per capita income, and other indices. Thus, countries with medium HDI have most of the cases and deaths, but countries with very high HDI have comparatively very few cases and deaths. Dramatically, cervical cancer cases in low HDI countries are similar to those in very high HDI countries, but death cases are high [8]. According to the World Health Organization (WHO), developed countries reported approximately 70% of cervical cancers and 90% of deaths [66]. In 2018, about 570 000 women were diagnosed, and almost 54.57% of the patients died, according to the WHO report. Although death rates have increased by 4% in recent times, developed countries have reduced their death rates in large numbers. However, women from 42 underdeveloped countries are most likely affected by cervical cancer [11]. About 84% of cases and 88% of deaths have been recorded in underdeveloped countries [8]. Squamous cell carcinoma (80%), adenocarcinoma (15%), adenosquamous carcinoma (3–5%), small cell carcinoma, and neuroendocrine cancer are the most frequent forms of cervical cancer [9].

Due to the extreme death rate, many methods have been developed to identify pre‐stage of cervical cancer so that the chances of survival can be improved by early diagnosis and early treatment of cervix cells. Different computer‐aided screening methods are used to detect an infected cervix before it has transformed into an invasive cancer. This computer‐assisted diagnosis (CAD) system is common, simple to use, and less time‐consuming. The Pap test (Pap smear), liquid‐based cytology (LBC), HPV DNA checking, visual inspection, and visual analysis of acetic acid are different screening procedures discussed in the literature [12].

For the past 50 years, the Pap test has lowered the case and death rate of cervical cancer in the US [9, 12]. The Pap test demanded a high amount of test capital, which has limited its use in developing countries. In the Pap test with conventional cytology, the cells are carefully scratched out of the ectocervix and endocervix by trained, skilled pathologists with a spatula or brush and smeared under a microscope slide [12]. It has a relatively low sensitivity of 54% but a high precision of 94%[13].

In LBC, cells are obtained in an identical method to a conventional Pap test. Samples are smeared into a liquid of maintained space. After filters or centrifuges, the sample is passed to a slide. It is a procedure that is more costly than traditional cytology and requires extra resources and expensive tools. No difference in relative sensitivity and precision from the traditional way was found in LBC, but there is a benefit in evaluating high‐risk HPV in the cervix cells. Moreover, in LBC, proper sample selection is not feasible [12, 14].

Automated Pap analysis helps to minimize errors by analyzing Pap smear slides using the CAD tool. AutoPap and AutoCyte Screen are two automatic Pap types. In AutoPap, centered on an algorithm, the sample of the slide is checked and graded. While it does not explain which of the cells is irregular, it may describe the cervix's actual condition [15].

Methods for visual screening are easy and can be carried out by a professional doctor or nurse, are comparatively cheap, do not require laboratory space, and have instant results. The different visual analysis types are as follows: unaided visual inspection and visual inspection with acetic acid (VIA) treatment. It is a representation of the cervix without acetic acid in the human eye. This reveals bad performance [16–18].

To identify pre‐cancerous lesions, visual inspection after application of 3–5% acetic acid is performed. It can also be identified with the human eye, known as cervicoscopy, or immediate visual inspection. Changeable coagulation of cellular proteins is caused by applying 3–5% acetic acid and invasive cancer regions containing a significant number of the same cells in the epithelial cells [19, 20]. In order to correctly detect pre‐cancerous lesions, VIA could do better than the Pap test [21]. Between 45% and 79% of women at high risk of developing cervical cancer have been correctly detected by qualified doctors and mid‐level providers [22]. VIA indicates higher false‐positive cases than the Pap approach.

However, it is hard to find professional pathologists to identify pre‐cancerous cervix cells by the naked eye. Community and researchers developed Internet of Things– (IoT‐) [23] and Internet of Healthcare Things– (IoHT‐) [24] based devices for pre‐screening. IoHT‐based devices work for Pap‐smear images and classify normal and abnormal images only. But there is a need of a system where women can have pre‐screening of cervical cancer at low cost with timely accurate diagnosis. The major challenge in such implementation requires computer vision and computational resources along with knowledge of medical sciences. Low resource and cost‐effective settings are required with highly secured and confidential data for preliminary screening tests. If the test results are positive, then the patient may be recommended or referred for colposcopy and further treatment with expert doctors. This preliminary screening may help the patient in reducing the chances of cervical cancer and can be treated by midwives and nurses. This may reduce the burden on the experts and may help the patient in early diagnosis.

Colposcopy or CIN images of the patients are hard to get but Intel and MobileODT through a competition on Kaggle in 2017 classified different types of cervix to detect the precancerous conditions of patients [25].

With continued improvement in screening methods and HPV vaccination programs in developed countries, the disparity of burden between developed countries and women in resource‐poor settings becomes even more profound. Nearly 85% of cervical cancer deaths occur in low‐ and middle‐income‐countries, and cervical cancer is a leading cause of cancer death in women in the developing world. If women are timely screened once after the age of 35 years, there is a 70% reduction in the risk of death due to cervical cancer. This can also reduce the disparity among women in developed countries and developing countries. The sentiments of women in developing countries may be hurt while to undergo physical examination. The women's hesitant nature gives us a high need to have a portable, wireless, and confidential device to have pre‐screening by midwives, nearby healthcare centers, or by themselves. The persistent HPV infection may lead to cervix cancer. The timely pre‐screening may define the important determinants and timely clearance with small treatments, which further reduces the viral persistence that may lead to cervical cancer. Recent image capturing technology can be used to make a portable, low‐cost, wireless, and confidential device that can be easily accessed and used by nearby healthcare centers.

8.2 Literature Review

IoT technology is an evolving healthcare field that has decreased patient waiting time and ensures treatment quality, even at home, by automating real‐time procedures. Various medical imaging experiments have been performed using IoT, known as Internet of Medical Things (IoMT), to pave the way for automation approaches.

He et al. explore cancer diagnostic approaches using deep learning algorithms such as convolutional neural networks (CNNs) and autoencoders (AE) [65]. To detect cancer, images of multiple organs or cells are used (such as lung, colon, breast, cervical). Various pre‐trained models such as VGG16, ResNet, and GoogleNet are used to distinguish between benign and malignant breast cells [26]. These pre‐trained models used average pooling and are attached to a completely connected layer. In a study [27], first of all Pap smear images were taken of the cervical cells from the Herlev data set. Then they introduced random forest (a machine learning algorithm) to classify normal and abnormal images. They extracted features depending on weight and used a 10‐fold cross‐validation for training. A custom CNN model called CXNN for classification has been created by Sun et al. [27]. They tuned optimization and training hyperparameters for CXNN.

Lu and Liu [28] explain clinical treatment and remote monitoring of ECG in real‐time using IoT in medical science. While they offer the concept of integrating IoT in healthcare, they do not explain IoT devices or how they are linked. In Ijaz et al. [29], a hybrid prediction model (HPM) is employed to provide early prediction of type 2 diabetes and hypertension using machine learning algorithms. The HPM of the analysis can be implemented using the IoMT, which stores evaluated risk factors on a safe remote server and provides the patient with the proper course. The authors suggest an IoT‐based android app for future work but don't explain how data is gathered by the sensor and linked to the mobile app. For future work, they suggest an IoT‐based android app, but they do not explain how the sensor captures or links information to the app. Rahman et al. [30] describe how the diagnosed cancer patients should maintain a secure routine during treatment. However, covering patients' daily lives is difficult, so many IoT‐based models have been developed to obtain a real‐time report of patients' conditions and activities. This study shows the identification of the state of the skeletons and the exercise of cancer patients; however, how this addresses cancer patients' issues is unclear. An IoT network system employed in Onasanya and Elshakankiri [23] mainly focuses on the wireless topology of different devices for diagnosis, recovery, and patient management. It is done by the analysis of health data obtained by IoT network systems and other mobile devices to help health professionals and make urgent assessments on cancer conditions.

Chandy [31] discusses IoT integration for multiple diagnostic systems of medical imaging such as MRI, x‐rays, CT, and ultrasound. However, there is still a need for further work on encryption algorithms to have a secure IoT medical device so as to avoid any misuse of data. Onasanya and Elshakankiri [32] created a smart healthcare infrastructure, especially for patients with cancer. They divided their entire solution into five‐layer approaches and joined them to construct an efficient, interconnected IoT framework. Specifically, these layers are a service layer for identification, tracking, and follow‐up, a data center layer for capturing cancer patient data, a cancer treatment layer for visualizing patient reports, a hospital layer for moving patient status to a doctor or nurse, and a protection layer for preventing unexpected hospital incidents.

Single‐board computers are developed by Parra et al. [33] to offer a low‐cost diagnosis for underdeveloped countries using a Raspberry Pi (RP) computer. They have developed two devices to diagnose cervix cell before turning into invasive cancer with real‐time results. One is based on visual inspection of the cervix's nuclei high‐resolution image implemented by IoT devices with deep learning algorithms for classifying cervix cells named PiHRME. Moreover, another molecular level test of cervical cells is also implemented by IoT to identify cells' current condition, called PiReader. Both are easy to use and cost half of the previous manual approaches. Although it is cost‐effective to use the Raspberry Pi computer, it can't be used to store and train with a lot of images. Khamparia et al. [24] employ the IoHT for the classification of cervical cell images collected from a Pap‐dating approach. In order to accomplish the mission, they have merged both IoT and image classification algorithms. To distinguish images, they use both machine learning and deep learning algorithms. Transfer learning was also used to reuse the pre‐developed knowledge of models. They claim a 97% accuracy because the data set is very limited, which causes the trained model to be overfitted. Cervical cancer screening will need a huge data set to train a robust model, which is still a concern for researchers.

8.3 Methodology

8.3.1 IoT Device for Image Acquisition

In this part, we describe an IoT‐based cervix detection healthcare system. We have used a Raspberry Pi (RP) board in the device, with a Raspberry Pi camera and an ultrasonic sensor. Robotics equipment is used in the device, which open the labia minora (LM). A labia minora and cervix detection algorithm is implemented in that RP board for capturing the correct organs. At first, the camera will take a photo to find the labia minora because it is the front part of cervix. After capturing the photo, the algorithm will detect the position of the labia minora (LM). After fixing the position of LM, it will be treated as an object. Then we will use ultrasonic sensor to measure the distance from the sensor to LM. We will measure the distance in order to correctly open the vagina by using the medical equipment, which will come from the IoT device, and it will work automatically, without any needed assistance. So if the equipment doesn't get accurate measure and location, then it will not be able to open the vagina. After opening the vagina, the camera will take a photo of the cervix. Then, the algorithm will work to detect cervix precancerous condition automatically. Figure 8.3 describes the total communication processes of IoT device elements with proper direction and command. The following subsections describe all the devices, step by step.

8.3.1.1 Raspberry Pi

In the United Kingdom, the RP foundation developed RP, which is a series of small single‐board computers launched in 2012. It is known as minicomputer, which performs computational processes. It is widely used for real‐time image or video processing, IoT‐based applications, and robotics‐based applications. In this board, we can add a camera, a sensor to measure distance, and other important devices. The Raspberry Pi includes a 1.2 GHz quad‐core 1.4 GHz, 1 GB RAM, the Broadcom SoC, GPU, built‐in Wi‐Fi and Bluetooth 4.0, and 10/100 Mbps Ethernet. Most people use it to build hardware projects, to make home automation, to process algorithms, and so on [34, 35]. As of 2016, about eight million Raspberry Pi had been sold, and it is projected to be one the most popular single‐board computers in the world [36]. We have implemented RP to detect and open the labia minora and at last detect the cervix using a pretrained model in the board (Figure 8.4).

Camera: Raspberry Pi has an embedded camera module (version 2). It has a Sony IMX219 eight megapixel camera sensor, and it's able to deliver almost five‐megapixel resolution of image or 1080p high‐definition video at 30 frames per second and still shots up to 2592 × 1944 resolution. Time lapse, slow motion video, IP CCTV and monitoring, and more are included in this module. The camera module was attached to a Raspberry Pi by a 15 pin ribbon cable and to the dedicated 15 pin MIPI camera serial interface, which was designed for interfacing to cameras and has a BCM2835 processor [37, 38].

8.3.1.2 Sensor

The ultrasonic sensor HC‐SR04 is used to measure the distance of an object from the sensor. This sensor is able to measure distances from 2 mm (0.02 m) to 400 cm (4 m). This sensor is the most popular sensor used in many applications and devices where we need to measure distances. Put another way, we can say an ultrasonic sensor is an electronic device that measures distances by emitting ultrasonic sound waves and convert this waves into an electrical signal. It is generally used with microcontroller and microprocessor platforms such as Arduino, ARM, PIC, and Raspberry Pi. Ultrasonic sensors are used in IoT‐ and robotic‐based platforms for detecting the targeted object, as well as in manufacturing technologies. Notably, ultrasonic technology has enabled in the medical sector or health sector to produce images of internal organs, to detect tumors, and to ensure the health of babies in the womb [39]. Now, we can use an ultrasonic sensor with RP [40]. RP works at 3.3 V logic, but the ultrasonic sensor HC‐SR04 works at 5 V. Thus, if we want to connect the ultrasonic sensor with this RP, then we have to connect an echo pin to RP before connecting it to the sensor. At first we have to connect the trig pin of the ultrasonic sensor with the physical pin GPIO23 of the RP. Then we have to use a combination of 680 Ω and 1.5 kΩ resistor for converting the echo pin to 3.3 V logic. And we need to connect it to a physical pin GPIO24 of the RP. Then the problem will be solved. Finally we have to write programming algorithms on Raspberry Pi to enable and perform the required task by the ultrasonic sensor.

8.3.1.3 Equipment

The IoT‐ and robotics‐based equipment used will be able to close or open the vagina carefully as it is a very sensitive organ of women. The equipment can move left, right, up, and down using the direction from RP. RP is directed by using the data of the sensor and algorithmic calculations.

After taking photos of the cervix, the device will store the image, storage embedded on RP board, and finally a trained machine learning algorithm will produce the desired diagnosis for cervical cancer. This algorithm will be implemented in the Raspberry Pi board to classify the cervix type using the image. Now this result and image will be sent to a cloud repository from RP storage with regard to the result about the patient. We have to install a cloud repository app using an Internet connection to the RP board. For details, see Doshi et al. [41] Finally the result and corresponding data is stored for both patient and the health team's supervision.

8.4 Use Case of Real‐Time Remote Monitoring System

The major challenges in the area of real‐time applications of healthcare is privacy, scalability, and compliance to transfer patients' data from one to another network. Another major challenge for IoMT is to allocate resources such as doctors and midwives to the patient immediately and locate everything using tracking devices. Cloud storage of a cervical cancer patient data must have the following features (Figure 8.5).

8.5 Conclusion

In the current scenario, we require a reasonable trade‐off between the burden and benefits of screening. Cytology tests and colposcopies increase the burden at both ends: patient as well as experts. Thus, preliminary diagnosis may help women with the recommendation whether they should go for colposcopy or not. Apart from this, it may help to take care of women's sentiments by providing an automated low‐cost, secured, and portable device in nearby healthcare centers which can be handled by midwives. The recommender system in the proposed methodology may help the patient with the appropriate advice for screening intervals if required and future actions to reduce incidence rate. If CIN I is diagnosed in the patient, the standard treatment of recommendation includes monitoring for progression and may reduce the incidence of CIN II and III with help of remote monitoring. Many developing countries have no cervical cancer screening programs due to lack of medical professionals and other resources. The proposed methodology will help the government and policy makers to have cervical cancer awareness programs, which will help in reducing the incidence of cervical cancer.

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