Neural Network Can Diagnose Covid-19 from Chest X-Rays

• New study is 98.4% accurate at detecting Covid-19 from X-rays.
• Researchers trained a convolutional neural network on Kaggle dataset.
• The hope is that the technology can be used to quickly and effectively identify Covid-19 patients.

As the Covid-19 pandemic continues to evolve, there is a pressing need for a faster diagnostic system. Testing kit shortages, virus mutations, and soaring numbers of cases have overwhelmed health care systems worldwide. Even when a good testing policy is in place, lab testing is arduous, expensive, and time consuming. Cheap antigen tests, which can give results in 30 seconds, are widely available but suffer from low sensitivity; The tests correctly identifying just 75% of Covid-19 cases a week after symptoms start [2].

Shashwat Sanket and colleagues set out to find an easy, fast, and accurate alternative using simple chest X-ray images. The team found that bilateral changes seen in chest X-rays of patients with Covid-19 can be analyzed and classified without a radiologist’s interpretation, using Convolutional Neural Networks (CNNs). The study, published in the September issue of Multimedia tools and Applications, successfully trained a CNN to accurately diagnose Covid-19 from Chest X-Rays, achieving an impressive 98.4% classification accuracy.. The journal article, titled Detection of novel coronavirus from chest X-rays using deep convolutional neural networks, shows some exciting promise in the ongoing efforts to find ways to detect Covid-19 quickly and effectively, 

What are Convolutional Neural Networks?

A convolutional neural network (CNN) is a Deep Learning algorithm that resembles the response of neurons in the visual cortex. The algorithm takes an input image and weighs the relative importance of various aspects in the image. The neurons overlap to span the entire field of vision, comprising a completely connected network where neurons in one layer link to neurons in other layers. The multilayered CNN includes an input layer, an output layer, and several hidden layers. A simple process called pooling keeps the most important features while reducing the dimensionality of the feature map. 

One major advantage of CNNs is that, compared to other classification algorithms, the required pre-processing is much lower. In addition, CNNs use regularized weights over fewer parameters. This avoids the exploding gradient and vanishing gradient problems of traditional neural networks during backpropagation.

Data Prep

The study began with a Kaggle dataset containing radiography images. As well as chest X-ray images for 219 COVID-19 positive cases, the dataset also contained 1341 normal chest X-rays and 1345 viral pneumonia images. Random selection was used to reduce the normal and viral pneumonia images to a balanced 219 each. The model, which the authors dubbed CovCNNl, was trained with augmented chest X-ray images; The raw images were standardized with each other using transformations like shearing, shifting and rotation. They were also converted to the same size: 224 × 224 × 3 pixels. Following the augmentation, the dataset was split into 525 images for training and 132 images for testing. The following image, from the study authors, demonstrates how the augmented images appear. Image a in the top row shows how Covid-19 appears on an x-ray, in comparison to four normal chest X-rays:

Seven existing pre-trained transfer learning models were used in the study, including ResNet-101 (a 101 layers deep CNN), Xception (71 layers deep), and VGG-16, which is widely used in image classification problems but painfully slow to train [3]. Transfer learning takes lessons learned from previous classification problems and transfers that knowledge to a new task—in this case, correctly identifying COVID-19 patients.

Results

Four variant CovCNN models were tested for effectiveness with several metrics, including: accuracy, F1-score, sensitivity, and specificity. The F1 score is a combination of recall and precision; Sensitivity is the true positive rate—the proportion of correctly predicted positive cases; Specificity is the proportion of correctly identified negative cases. The CovCNN_4 model outperformed all the other models, achieving 98.48% accuracy, 100% sensitivity, and 97.73% specificity. This fine-tuned deep network contained 15 layers, stacked sequentially with increasing filter sizes. This next image shows the layout of the model:

The authors conclude that their covCNN_4 model can be employed to assist medical practitioners and radiologists with faster, more accurate Covid-19 diagnosis, as well as follow up cases. In addition, they recommend that their model’s accuracy can be further improved by “fusion of CNN and pre-trained model features”.

References

CNN Images: Adobe Creative Cloud

[1] Detection of novel coronavirus from chest X-rays using deep convolu…
[2] Fast coronavirus tests: what they can and can’t do
[3] VGG16 – Convolutional Network for Classification and Detection

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How Businesses Are Using Data Analytics for Better Operational Efficiency

As the market gets more competitive with time, businesses are altering their strategies to sustain and cater to changing customer needs better. The present era customers have smartened up considerably! They know what they want, and luring them with glitzy ads and lofty marketing pitches does not cut much ice anymore. They want better value for money and an enhanced experience. So, businesses need to offer better service, enhance product quality, and become more productive and efficient. 

Data analytics is a big weapon for enhancing the operational efficacy of businesses.

Nowadays, businesses of varying types and sizes are resorting to data analytics applications to enhance efficiency and productivity levels. They obtain data from a number of sources- both offline and online. This huge amount of data is then compiled and analysed by using specialized BI solutions. The resultant reports and insights help the businesses to get a better grasp of various nuances of operations. They resort to using cutting-edge data analytics applications, including power bi solutions.

How using data analytics software and applications can be useful for businesses. 

• It helps businesses identify market needs- The BI and data analytics tools can be useful for identifying market needs. Data obtained from online and offline customer surveys, polls and other types of feedback are compiled and analysed by such applications. The results can help businesses understand the precise needs of the market. This can vary from one location to another. When businesses can understand regional market needs better, they can tweak their production plan accordingly. It proves to be beneficial in the long run. 

It aids the brands to detect and eliminate Supply Chain hurdles- For a brand manufacturing physical products, supply chain optimization can prove to be tedious. Logistics related issues can crop unexpectedly, hampering the sales and supply chain system. Issues that can affect the supply chain include shipping delays, damage to fragile items, whether caused by hassles, employee issues, etc. This is where data analytics tools like Power BI can come in handy. 

• The data collected through sensors, cloud services and wearable devices are analysed by such applications. The generated reports help power bi consultants figure out the existing loopholes leading to disruptions in the supply chain. They can thereafter come up with strategies to tackle and eliminate such issues.  

It helps identify and resolve Team-coordination issues- Sometimes, a company may find it hard to achieve its operational target owing to improper and inadequate sync between various departments. The departments like HR, sales and advertising may not have good sync with one another. This can lead to inefficient resource sharing. For the management, it may be hard to figure out these internal glitches. However, hiring a data analytics expert can be helpful in resolving such conditions.

• A veteran power bi developer can use the tool to analyze collected data and find out the issues leading to a lack of sync between various departments. Thereafter, suitable remedial measures can be taken to boost resource sharing, and that can help augment efficiency. 

• It helps detect employee and team productivity issues- Not everyone in a team in a company has equal efficacy and productivity. A senior team member and employee may work smarter and faster than newly inducted ones. Sometimes, disgruntled employees may deliberately work in an unproductive way. The overall output gets affected when there are such issues affecting the productivity and efficacy of the employees in a company. 

For the management, checking the efficacy of every single employee may not be easy. In a large-sized organization, it is near impossible. However, identifying employee efficacy and productivity becomes easier when a suitable data analytics solution is used. Hiring a power bi development professional can be handy in such situations. By identifying factors leading to employee productivity deficit, corrective measures can be deployed.

• It helps detect third-party/vendor related issues- In many companies, working with third-party vendors and suppliers becomes necessary. Businesses may rely on such vendors for the supply of raw materials, and they also hire such vendors to outsource specific operations. Sometimes, the operational output of the company may get affected owing to reliance on a vendor not suited for its needs. The suitability of such vendors can be understood well by deploying data analytics services.  

• It aids in understanding speed related issues- Sluggishness in production may affect the output in a business setup, for sure. Production or manufacturing involves a number of stages, and delay in one or more stages can affect productivity and efficacy. It may be hard for the company management to fathom what is causing the delay in the production workflow. The reasons can be worn out by machinery or unskilled workforce. Deploying the latest data analytics solutions can be useful for detecting and resolving the issues affecting production speed.  
• It helps in detecting IT infrastructure issues- Sometimes, your business may find it hard to achieve operational targets owing to the usage of outdated or ageing IT infrastructure. It is both hardware and software related issues that affect output and efficiency. The legacy systems used in some organizations bottleneck the prowess of a skilled and efficient workforce- as it has been seen. Deploying the latest data analytics solutions helps the companies understand which part of the IT infrastructure is causing the deficit in output.  

It aids in understanding cost overrun factors- In every company, incurring a cost is a prerequisite for keeping the workflow alive. However, it is also necessary that the running expenditure of the workplace is kept within a limit. It can be hard to figure out if the money spent after departments like electricity, internet, sanitation etc., are being kept within a limit or overspending is taking place. Sometimes, hidden costs may be involved, which may skip scrutiny of the accounts departments. 

• When data analytics tools are used, it is easier to find out instances of cost overrun in such setups. The management then can take up corrective measures to ensure running cost is kept within feasible limits.  

Summing it up

Usage of data analytics tools like Power BI helps a company in figuring out issues that are bottlenecking productivity and output. The advanced data analysis and report generation capabilities of such tools help businesses fathom issues that can be hard to interpret and analyze otherwise. By using such tools, businesses can also make near accurate predictions about market dynamics and customer preferences. However, to leverage the full potential of such tools, hiring suitable data analytics professionals will be necessary.

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