Building the Internet of Things platform for smart maternal healthcare services with wearable devices and cloud computing

The United Nations’ Sustainable Development Goals (SDGs) set out to improve the quality of life of people in developed, emerging, and developing countries by covering social and economic aspects, with a focus on environmental sustainability. At the same time, data-driven technologies influence our lives in all areas and have caused fundamental economical and societal changes. This study presents a comprehensive literature review on how data-driven approaches have enabled or inhibited the successful achievement of the 17 SDGs to date. Our findings show that data-driven analytics and tools contribute to achieving the 17 SDGs, e.g., by making information more reliable, supporting better-informed decision-making, implementing data-based policies, prioritizing actions, and optimizing the allocation of resources. Based on a qualitative content analysis, results were aggregated into a conceptual framework, including the following categories: (1) uses of data-driven methods (e.g., monitoring, measurement, mapping or modeling, forecasting, risk assessment, and planning purposes), (2) resulting positive effects, (3) arising challenges, and (4) recommendations for action to overcome these challenges. Despite positive effects and versatile applications, problems such as data gaps, data biases, high energy consumption of computational resources, ethical concerns, privacy, ownership, and security issues stand in the way of achieving the 17 SDGs.

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The Contribution of Data-Driven Technologies in Achieving the Sustainable Development Goals

One of the biggest difficulties facing medicine today is cancer. Lung cancer along with colon cancer, stomach cancer and liver cancer, are the four most dangerous cancers. If the disease is detected early and treated properly, it can prolong the patient’s life. Today, many tasks in many fields, including medicine, can be resolved by using deep learning techniques. This paper proposes to modify the Deep Neural Network transfer learning for the lung and colon cancer classification based on GoogLeNet. Specifically, the main idea of the inception module of GoogLeNet that is running multiple operations (pooling, convolution) with multiple filter sizes in parallel so that we do not have to face any trade-off. The second advantage of the inception module is dimensionality reduction of feature maps and over parameterization dealing. The output of classification was adjusted to 3 or 2 classes due to the required classes of lung and colon problems. The accuracy of the proposed method is 99.66% and 100% in the lung and colon image dataset, respectively. The results of the proposed method are better than the other methods such as VGG16, Resnet50, NASNetMobile and original GoogLeNet. 

Lung and Colon Tumor Classification Based on Transfer Learning-Based Techniques

The most precarious cancer in humans is lung cancer. With the problems arising in low accuracy and poor effect of lung nodule segmentation, U-Net-based semantic segmentation approaches are widely used. The paper aims to compare the different types of U-Net models, such as U-Net2D, R2U-Net2D, U-Net++, and Attention U-Net to get the best model out of these. The results from the experiments show that U-Net2D gave the best performance with an accuracy of 99.38%, 74.34% mean IOU, and 0.01 binary cross-entropy loss. Also, it is observed that the training and validation accuracy are approximately the same, thus showing no over-fitting problems, which can aid radiologists in detecting pulmonary lung nodules effectively.

Implementation of Different U-Net Architectures for Segmentation of Lung Cancer CT Images

Lung cancer is one of the deadly diseases whose prediction is required to reduce the death rate. So, Artificial intelligence is used on CT scan images are used for achieving better accuracy in an automated manner. Deep Learning is one of the emerging trends for predicting values. Convolution Neural Networks is one of the deep learning algorithms which implemented to sample produces better outcomes as compared to other machine learning algorithms. In this paper, the data-set hasbeen taken with 1000 images of chest scans for different types of lung cancers such as Adenocarcinoma, Large Cell Carcinoma andSquamous Cell Carcinoma. Multiple machine learning algorithmshas been compared and then it has been confirmed that CNN is one of the best among all to check the accuracy of the prediction. The paper includes VGG - 16 implemented on data set having different types of Lung Cancer and thus helping to check the severity and precautions for the same in a distinct manner.

Prediction of Lung Cancer using Convolution Neural Networks

Lung nodules are an early manifestation of lung cancer. Early detection of lung nodules plays a vital role in improving the survival rate of patients. Computed tomography (CT) has fast scanning speed, high image size, and can capture tiny areas. The application of CT for clinical diagnosis is an effective method. We use convolutional neural network (CNN) to study the algorithm of lung nodule detection and diagnosis based on CT images. This paper is based on a lung nodule segmentation network combining VGG-16 and dilated convolution. And compared with traditional lung nodule segmentation methods and Inception v2, XOR evaluation coefficient, Hausdorff distance, Jaccard similarity coefficient, accuracy, sensitivity and specificity were used as segmentation evaluation indexes. VGG-16 is superior to traditional image processing methods in all indicators. The accuracy of VGG-16 is as high as 0.971, the false detection rate is only 0.101, and the missed detection rate is only 0.074. The segmented image result is closest to Ground Truth, and there is no problem of incorrect segmentation of lung parenchyma and lung nodules. The VGG-16 proposed in this paper improves the accuracy of segmenting lung nodules, and is superior to traditional image processing methods in various performance indicators, which can effectively help experts diagnose lung nodules.

Lung Cancer Detection using a Dilated CNN with VGG16

In recent years, the industry of wearable devices for pregnancies has been developing rapidly. The devices ranges from foetal monitors to multi-functional health examination instruments, which helped monitoring and management of maternal health indicators such as foetal heart rate, blood glucose, and blood pressure in home. Pregnant women and obstetricians are bounded together by wearable devices in an unprecedented way. With the universal use of the Internet of Things technology, the Smart Maternal construction started to raise people’s attention. This paper investigated the impact of the state-of-the-art monitoring techniques such as Internet of Things for smart maternal healthcare services. We have performed an experimental analysis based on a dataset of 315 samples of pregnant women using questionnaires. The results implies there are generally a large impact that pregnant women’s recognition and acceptance of using wearable IoT devices during pregnancy.

The Impact of Healthcare Monitoring Technologies for Better Pregnancy

As an essential part of IoTs, wireless sensor networks are inseparable from its future development. However, sensor nodes are usually deployed in some complex and harsh areas, and it is difficult to replacing batteries manually. Thus, energy is the most key factor affecting the life cycle of wireless sensor networks. The energy loss occurs during the process of recharging and the movement among the sensors. To maintain the normal operation of the networks, sensors need to be recharged by deploying chargers. Therefore, we aim to find the shortest route. This paper studies two optimal routes planning of chargers by developing models based on genetic algorithm. The first route planning is in the case of only one mobile charger in the network. The second is based on the network with four chargers. After performing millions of iterations, the optimal dispatching scheme of mobile chargers was obtained.

Shijie Shi

Papers

Lung Cancer Detection using a Dilated CNN with VGG16

The Impact of Healthcare Monitoring Technologies for Better Pregnancy

Charging Route Planning for Wireless Rechargeable Sensor Networks