Artificial intelligence (AI) has been applied widely in our daily lives in a variety of ways with numerous successful stories. AI has also contributed to dealing with the coronavirus disease (COVID-19) pandemic, which has been happening around the globe. This paper presents a survey of AI methods being used in various applications in the fight against the COVID-19 outbreak and outlines the crucial roles of AI research in this unprecedented battle. We touch on a number of areas where AI plays as an essential component, from medical image processing, data analytics, text mining and natural language processing, the Internet of Things, to computational biology and medicine. A summary of COVID-19 related data sources that are available for research purposes is also presented. Research directions on exploring the potentials of AI and enhancing its capabilities and power in the battle are thoroughly discussed. We highlight 13 groups of problems related to the COVID-19 pandemic and point out promising AI methods and tools that can be used to solve those problems. It is envisaged that this study will provide AI researchers and the wider community an overview of the current status of AI applications and motivate researchers in harnessing AI potentials in the fight against COVID-19.

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Artificial Intelligence in the Battle against Coronavirus (COVID-19): A Survey and Future Research Directions

Machine learning and artificial intelligence based Diabetes Mellitus detection and self-management: A systematic review

Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011-2022)

COVID-19 has emerged as a global crisis with unprecedented socio-economic challenges, jeopardizing our lives and livelihoods for years to come. The unavailability of vaccines for COVID-19 has rendered rapid testing of the population instrumental in order to contain the exponential rise in cases of infection. Shortage of RT-PCR test kits and delays in obtaining test results calls for alternative methods of rapid and reliable diagnosis. In this article, we propose a novel deep learning-based solution using chest X-rays which can help in rapid triaging of COVID-19 patients. The proposed solution uses image enhancement, image segmentation, and employs a modified stacked ensemble model consisting of four CNN base-learners along with Naive Bayes as meta-learner to classify chest X-rays into three classes viz. COVID-19, pneumonia, and normal. An effective pruning strategy as introduced in the proposed framework results in increased model performance, generalizability, and decreased model complexity. We incorporate explainability in our article by using Grad-CAM visualization in order to establish trust in the medical AI system. Furthermore, we evaluate multiple state-of-the-art GAN architectures and their ability to generate realistic synthetic samples of COVID-19 chest X-rays to deal with limited numbers of training samples. The proposed solution significantly outperforms existing methods, with 98.67% accuracy, 0.98 Kappa score, and F-1 scores of 100, 98, and 98 for COVID-19, normal, and pneumonia classes, respectively, on standard datasets. The proposed solution can be used as one element of patient evaluation along with gold-standard clinical and laboratory testing.

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COVIDScreen: explainable deep learning framework for differential diagnosis of COVID-19 using chest X-rays.

Deep learning algorithms have become the first choice as an approach to medical image analysis, face recognition, and emotion recognition. In this survey, several deep-learning-based approaches applied to breast cancer, cervical cancer, brain tumor, colon and lung cancers are studied and reviewed. Deep learning has been applied in almost all of the imaging modalities used for cervical and breast cancers and MRIs for the brain tumor. The result of the review process indicated that deep learning methods have achieved state-of-the-art in tumor detection, segmentation, feature extraction and classification. As presented in this paper, the deep learning approaches were used in three different modes that include training from scratch, transfer learning through freezing some layers of the deep learning network and modifying the architecture to reduce the number of parameters existing in the network. Moreover, the application of deep learning to imaging devices for the detection of various cancer cases has been studied by researchers affiliated to academic and medical institutes in economically developed countries; while, the study has not had much attention in Africa despite the dramatic soar of cancer risks in the continent.

https://www.mdpi.com/2313-433X/6/11/121/pdf

Deep Learning in Selected Cancers' Image Analysis-A Survey.

Diabetic Macular Edema (DME) is an advanced stage of Diabetic Retinopathy (DR) and can lead to permanent vision loss. Currently, it affects 26.7 million people globally and on account of such a huge number of DME cases and the limited number of ophthalmologists, it is desirable to automate the diagnosis process. Computer-assisted, deep learning based diagnosis could help in early detection, following which precision medication can help to mitigate the vision loss. Method In order to automate the screening of DME, we propose a novel DMENet Algorithm which is built on the pillars of Convolutional Neural Networks (CNNs). DMENet analyses the preprocessed color fundus images and passes it through a two-stage pipeline. The first stage detects the presence or absence of DME whereas the second stage takes only the positive cases and grades the images based on severity. In both the stages, we use a novel Hierarchical Ensemble of CNNs (HE-CNN). This paper uses two of the popular publicly available datasets IDRiD and MESSIDOR for classification. Preprocessing on the images is performed using morphological opening and gaussian kernel. The dataset is augmented to solve the class imbalance problem for better performance of the proposed model. Results The proposed methodology achieved an average Accuracy of 96.12%, Sensitivity of 96.32%, Specificity of 95.84%, and F-1 score of 0.9609 on MESSIDOR and IDRiD datasets. Conclusion These excellent results establish the validity of the proposed methodology for use in DME screening and solidifies the applicability of the HE-CNN classification technique in the domain of biomedical imaging.

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DMENet: Diabetic Macular Edema diagnosis using Hierarchical Ensemble of CNNs.

Cervical cancer affects 570,000 women globally and is among the most common causes of cancer-related deaths. Cervical cancer is caused due to the Human Papilloma Virus (HPV) which leads to abnormal growth of cells in the cervix region. Regular testing for HPV in women has helped reduce the death rate in developed countries. However, developing nations are still struggling to provide low-cost solutions due to the lack of affordable medical facilities. The skewed ratio of the oncologists to patients has also aggravated the problem. Motivated by the Deep Learning solutions in Bio-medical imaging, we propose a novel CervixNet methodology which performs image enhancement on cervigrams followed by Segmenting the Region of Interest (RoI) and then classifying the RoI to determine the appropriate treatment. For the classification task, a novel Hierarchical Convolutional Mixture of Experts (HCME) algorithm is proposed. HCME is capable of tackling the problem of overfitting, given that small datasets are an inherent problem in the field of biomedical imaging. Our proposed methodology has outperformed all the existing methodologies on publicly available Intel and Mobile-ODT Kaggle dataset giving an Accuracy of 96.77% and kappa score of 0.951. Hence, the results obtained validate our approach to provide first level screening at a low cost.

Cervical Cancer Diagnosis using CervixNet - A Deep Learning Approach

A new analytical approximate expression for K distribution is proposed by expanding it in terms of orthogonal associated Laguerre polynomial. The expansion is truncated after first three terms, which yields a fairly close approximation to K distribution. The advantage of the proposed approximation is that the analytical closed form expression for bit error rate can be easily derived. KL measure is used to show the accuracy of the proposed approximation. The proposed approximate probability density function and bit error rate work well within the desired range of the channel parameter $\alpha$ , which is $1 and corresponds to the scintillation index value ranging from 2 to 3. We have also demonstrated the utility of our approximation for other quality of service metric such as fade probability.

A Novel Approximation for K Distribution: Closed-Form BER Using DPSK Modulation in Free-Space Optical Communication

The human attention span is continuously decreasing, and the amount of time a person wants to spend on reading is declining at an alarming rate. Therefore, it is imperative to provide a quick glance of important news by generating a concise summary of the prominent news article, along with the most intuitive headline in line with the summary. When humans produce summaries of documents, they not only extract phrases and concatenate them but also produce new grammatical phrases or sentences that coincide with each other and capture the most significant information of the original article. Humans have an incredible ability to create abstractions; however, automatic summarization is a challenging problem. This paper aims to develop an end-to-end methodology that can generate brief summaries and crisp headlines that can capture the attention of readers and convey a significant amount of relevant information. In this paper, we propose a novel methodology known as SHEG, which is designed as a hybrid model. It works by integrating both extractive and abstractive mechanisms using a pipelined approach to produce a concise summary, which is then used for headline generation. Experiments were performed on publicly available datasets, viz. CNN/Daily Mail, Gigaword, and NEWSROOM. The results obtained validate our approach and demonstrate that the proposed SHEG model is effectively producing a concise summary as well as a captivating and fitting headline.

Rajeev Kumar Singh

Papers

COVIDScreen: explainable deep learning framework for differential diagnosis of COVID-19 using chest X-rays.

DMENet: Diabetic Macular Edema diagnosis using Hierarchical Ensemble of CNNs.

Cervical Cancer Diagnosis using CervixNet - A Deep Learning Approach

A Novel Approximation for K Distribution: Closed-Form BER Using DPSK Modulation in Free-Space Optical Communication

SHEG: summarization and headline generation of news articles using deep learning