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SARS-CoV-2, known as corona virus, causes COVID-19. It is an infectious disease first discovered in China in December 20191,2,3. World Health Organization (WHO) also declares it as a pandemic. Figure1 shows its detail structure3. This new virus quickly spread throughout the world. Its effect is transmitted to humans through their zoonotic flora. COVID-19's main clinical topographies are cough, sore throat, muscle pain, fever, and shortness of breath4,5. Normally, RT-PCR is used for COVID-19 detection. CT and X-ray have also vital roles in early and quick detection of COVID-196. However, RT-PCR has low sensitivity of about 60% -70% and even some times negative results are obtained7,8. It is observed that CT is a subtle approach to detecting COVID-19, and it may be a best screening means9.

Artificial intelligence and its subsets play a significant role in medicine and have recently expanded their prominence by being used as tool to assist physicians10,11,12. Deep learning techniques are also used with prominent results in many disease detections like skin cancer detection, breast cancer detection, and lung segmentation13,14. However, Due to limited resources and radiologists, providing clinicians to each hospital is a difficult task. Consequently, a need of automatic AI or machine learning methods is required to mitigate the issues. It can also be useful in reducing waiting time and test cost by removing RT-PCR kits. However, thorough pre-processing of CT images is necessary to achieve the best results. Poisson or Impulse noise during the acquisition process of these photos could have seriously damaged the image information15. To make post-processing tasks like object categorization and segmentation easier, it is essential to recover this lost information. Various filtering algorithms have been proposed to de-blur and to de-noise images in past. Standard Median Filter (SMF) is one of the most often used non-linear filters16.

A number of SMF modifications, including Weighted median and Center weighted median (CWM)17,18, have been proposed. The most widely used noise adaptive soft-switching median (NASM) was proposed in19, which achieved optimal results. However, if the noise density exceeds 50%, the quality of the recovered images degradedsignificantly. These methods are all non-adaptive and unable to distinguish between edge pixels, uncorrupted pixels, and corrupted pixels. Recent deep learning idea presented in20,21,22 performs well in recovering the images degraded by fixed value Impulse noise. However, its efficiency decreases with the increase in the noise density and in reduction of Poisson noise, which normally exist in CT images. Additionally, most of these methods are non-adaptive and fails while recovering Poisson noise degraded images. In the first phase of this study, layer discrimination with max/min intensities elimination with adaptive filtering window is proposed, which can handle high density Impulse and Poisson noise corrupted CT images. The proposed method has shown superior performance both visually and statistically.

Different deep learning methods are being utilized to detect COVID-19 automatically. To detect COVID-19 in CT scans, a deep learning model employing the COVIDX-Net model that consists of seven CNN models, was developed. This model has higher sensitivity, specificity and can detect COVID-19 with 91.7% accuracy23. Reference24 shows a deep learning model which obtains 92.4% results in detection of COVID-19. A ResNet50 model was proposed in25 which also achieved 98% results as well. All of these trials, nevertheless, took more time to diagnose and didn't produce the best outcomes because of information loss during the acquisition process. There are many studies on detection of COVID-19 that employ machine learning models with CT images26,27,28,29.A study presented in30proposes two different approaches with two systems each to diagnose tuberculosis from two datasets. In this study,initially, PCA) algorithm was employedto reduce the features dimensionality, aiming to extract the deep features. Then, SVM algorithm was used to for classifying features. This hybrid approachachieved an accuracy of 99.2%, a sensitivity of 99.23%, a specificity of 99.41%, and an AUC of 99.78%. Similarly, a study presented in31 utilizes different noise reduction techniques and compared the resultsby calculating qualitative visual inspection and quantitative parameters like Peak Signal-to-Noise Ratio (PSNR), Correlation Coefficient (Cr), and system complexity to determine the optimum denoising algorithm to be applied universally. However, these techniques manipulate all pixels whether they are contaminated by noise or not.An automated deep learning approach from Computed Tomography (CT) scan images to detect COVID-19 is proposed in32. In this method anisotropic diffusion techniques are used to de-noised the image and then CNN model is employed to train the dataset. At the end, different models including AlexNet, ResNet50, VGG16 and VGG19 have been evaluated in the experiments. This method worked well and achieved higher accuracy. However, when the images were contaminated with higher noise density, its performance suffered.Similarly, the authors in33 used four powerful pre-trained CNN models, VGG16, DenseNet121, ResNet50,and ResNet152, for the COVID-19 CT-scan binary classification task. In this method, a FastAI ResNet framework was designed to automatically find the best architecture using CT images. Additionally, a transfer learning techniques were used to overcome the large training time. This method achieved a higher F1 score of 96%. A deep learning method to detect COVID-19 using chest X-ray images was presented in 34. A dataset of 10,040 samples were used in this study. This model has a detection accuracy of 96.43% and a sensitivity of 93.68%.However, its performance dramatically decreases with higher density Poisson noise. A convolution neural networks method used for binary classification pneumonia-based conversion of VGG-19, Inception_V2, and decision tree model was presented in35. In this study, X-ray and CT scan images dataset that contains 360 images were used for COVID-19 detection. According to the findings, VGG-19, Inception_V2 and decision tree model illustrate high performance with accuracy of 91% than Inception_V2 (78%) and decision tree (60%) models.

In this paper, a paradigm for automatic COVID-19 screening that is based on assessment fusion is proposed. The effectiveness and efficiency of all baseline models were improved by our proposed model, which utilized the majority voting prediction technique to eliminate the mistakes of individual models. The proposed AFM model only needs chest X-ray images to diagnose COVID-19 in an accurate and speeding way.

The rest of the paper is organized as: The dataset is explained in section "Meterial and methods". section "Proposed method" explains our proposed approach and section "Results and Discussion" presents empirical results and analysis. section "Conclusion" describes conclusion and the specific contributions along with the future directions for improving the efficiency of the proposed work.

The rest is here:
A novel CT image de-noising and fusion based deep learning ... - Nature.com

Depending on your life, everybody has different opinions on artificial intelligence and its future. Some believed that it was just another fad that was going to die out soon. Whilst some believed there was a huge potential to implement it into our everyday lives.

At this point, its clear to say that AI is having a big impact on our lives and is here to stay.

With the recent advancements in AI technology such as ChatGPT and autonomous systems such as Baby AGI - we can stand on the continuous advancement of artificial intelligence in the future. It is nothing new. It's the same drastic change we saw with the arrival of computers, the internet, and smartphones.

A few years ago, there was a survey conducted with 6,000 customers in six countries, where only 36% of consumers were comfortable with businesses using AI and 72% expressed that they had some fear about the use of AI.

Although it is very interesting, it can also be concerning. Although we expect more to come in the future regarding AI, the big question is What are the ethics around it?.

The most developing and implemented area of AI development is machine learning. This allows models to learn and improve using past experience by exploring data and identifying patterns with little human intervention. Machine learning is used in different sectors, from finance to healthcare. We have virtual assistants such as Alexa, and now we have large language models such as ChatGPT.

So how do we determine the ethics around these AI applications, and how it will affect the economy and society?

There are a few ethical concerns surrounding AI:

1. Bias and Discrimination

Although data is the new oil and we have a lot of it, there are still concerns about AI being biased and discriminative with the data it has. For example, the use of facial recognition applications has proven to be highly biased and discriminative to certain ethnic groups, such as people with darker skin tones.

Although some of these facial recognition applications had high racial and gender bias, companies such as Amazon refused to stop selling the product to the government in 2018.

2. Privacy

Another concern around the use of AI applications is privacy. These applications require a vast amount of data to produce accurate outputs and have high performance. However, there are concerns regarding data collection, storage, and use.

3. Transparency

Although AI applications are inputted with data, there is a high concern about the transparency of how these AI applications come to their decision. The creators of these AI applications deal with a lack of transparency raising the question of who to hold accountable for the outcome.

4. Autonomous Applications

We have seen the birth of Baby AGI, an autonomous task manager. Autonomous applications have the ability to make decisions with the help of a human. This naturally opens eyes to the public on leaving the decision to be made by technology, which could be deemed ethically or morally wrong in society's eyes.

5. Job security

This concern has been an ongoing conversation since the birth of artificial intelligence. With more and more people seeing that technology can do their job, such as ChatGPT creating content and potentially replacing content creators - what are the social and economic consequences of implementing AI into our everyday lives?

In April 2021, the European Commission published its legislation on the Act of the use of AI. The act aimed to ensure that AI systems met fundamental rights and provided users and society with trust. It contained a framework that grouped AI systems into 4 risk areas; unacceptable risk, high risk, limited, and minimal or no risk. You can learn more about it here: European AI Act: The Simplified Breakdown.

Other countries such as Brazil also passed a bill in 2021 that created a legal framework around the use of AI. Therefore, we can see that countries and continents around the world are looking further into the use of AI and how it can be ethically used.

The fast advancements in AI will have to align with the proposed frameworks and standards. Companies who are building or implementing AI systems will have to follow ethical standards and conduct an assessment of the application to ensure transparency, and privacy and account for bias and discrimination.

These frameworks and standards will need to focus on data governance, documented, transparent, human oversight, and robust, accurate, cyber-safe AI systems. If companies fail to comply, they will, unfortunately, have to deal with fines and penalties.

The launch of ChatGPT and the development of general-purpose AI applications have prompted scientists and politicians to establish a legal and ethical framework to avoid any potential harm or impact of AI applications.

This year alone there have been many papers released on the use of AI and the ethics surrounding it. For example, Assessing the Transatlantic Race to Govern AI-Driven Decision-Making through a Comparative Lens. We will continue to see more and more papers getting released till governments conduct and publish a clear and concise framework for companies to implement.

Nisha Arya is a Data Scientist, Freelance Technical Writer and Community Manager at KDnuggets. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.

The rest is here:
The Ethics of AI: Navigating the Future of Intelligent Machines - KDnuggets