Screening for skin disease on your laptop: New Artificial Neural Network Design

Millions of people all across the world are going through contract skin conditions as well as rashes that may cause irritation, skin inflammation, and clog. Subsequently, this may lead you to redness, burning, itching, swelling, if you are not going to get it treated immediately. The main question to be asked is, with the introduction of Artificial Neural Network, can ANN be used for screening such skin diseases directly on your laptop?

Earlier detection of dermatologist ailments is important

Well, due to many reasons, earlier detection of dermatologist diseases has become very important in the world of the present day. However, the most major reason here is that the epidemics of skin diseases have the potential to lead to various major losses of life all around the world.

• Skin cancer is one of the most common reasons that can be fatal if not diagnosed earlier. Skin cancer is the most common type of cancer in the world which is striking about 1 out of 5 people aged 70. However, fortunately, about 90% of these cases can be cured, if these are diagnosed earlier.
• In a nutshell, to prevent skin cancer, we have to spot the problem on time.
• Fortunately, skin cancer is a type of cancer that you can see with ease. Unlike other types of cancers which commonly develop in the inner side of your body, skin cancer develops on the outside and more often detectable as well. That is why individuals can even examine their skin themselves if they are going to have the right tool for it.
• Even more, earlier detection of skin cancer can also save the life of a patient suffering from this problem. Also, learning what you should look for on your skin and the right tool for diagnosis can bring the power to detect skin cancer earlier. Ultimately, it will become easier for you to cure skin cancer way before it is going to become disfiguring, dangerous, and deadly.
• Besides skin cancer, there are various other types of skin diseases present that need to be detected earlier for proper and more effective treatment. Systematic Sclerosis is an effective example here to consider in this regard.

Automated diagnosis systems can come into action here

Even more, in developing countries, it is highly important to provide the facility of automated diagnosis or screening systems. It is because such systems can help in reducing the manual efforts as well as the consumption of time of both patients and dermatologists.

A computer-based automated system for the detection of skin diseases is possible by taking the approach of a computer version.

Screening for skin disease on your laptop

The Biomedical Engineering Department’s founding chair at the University of Houston has reported a new neural network design with the potential to provide the Systematic Sclerosis earlier diagnosis. Basically, Systematic Sclerosis is an autoimmune disease that is rare. This disease is marked by fibrous or hardened skin as well as internal organs.

However, the proposed network would be implemented by using a standard laptop or PC with standard specifications like 2.5 GHz core i7, intel. This network has the potential to differentiate between photographs of healthy and diseased skin. It means the proposed network can detect skin affected with Systematic Sclerosis immediately.

Proposal of a new artificial neural network design

According to biomedical engineering’s chair professor Metin Akay, John S. Dunn Endowed, the primary study for this network is intended to depict the efficacy and efficiency of the architecture of the proposed network. It is holding the promise in the SSC characterization. Their work has been published in the Engineering in Medicine and Biology IEEE Open Journal.

However, he further said that they believe that the architecture of the proposed network is able to be implemented in any clinical setting in an easier way. It has the potential to provide an inexpensive, simple yet accurate screening tool to be used for SC.

Early diagnosis of SSC is critical for parents

Undoubtedly, people who are suffering from Systematic Sclerosis know well the diagnosis of this problem is essential, but more often it is elusive. Various research studies have suggested that organ involvement in this problem may occur way earlier than someone expected. It can happen even in the very early stage of this problem.

Fortunately, the early diagnosis of this problem and identifying the extent of the progression of this disease can pose a substantial challenge for dermatologists or physicians, even at experienced and expert centers. This as a result will delay the management and therapy of SC.

However, in the field of artificial intelligence, deep learning can organize algorithms in layers, i.e., an artificial neural network. This will then be able to make its own intelligent and quick decisions. While accelerating its learning process, this new network has been trained using MobileNetV2 parameters, a mobile version application that is amazingly trained prior on the dataset of ImageNet about 1.4 million images.

With the scanning of these images, the neural network architecture is used to learn from its existing images. According to Akay, the network then will compare the present image with the precious ones to decide the stage of the new image. Then it will decide and let you know whether the current image is normal, at the late stage of the disease, or it is on the earlier stage of the problem at present.

The new Artificial Neural Network design is a blend of technologies

Whenever it comes to networks for deep learning, then from all of the available options, CNN also known as Convolutional Neural Networks are the ones that are being used in the field of medicine, engineering, and biology most commonly.

However, the success of Convolutional Neural Networks has been limited in the applications in the biomedical field. The major reason behind this limitation is the restricted size of networks as well as training sets available.

Therefore, Yasemin Akay and partner Akay decided to blend two technologies. This blend is just meant to avoid or overcome the limitations of Convolutional Neural Networks. However, in this architecture, Akay has combined a modified architecture of CNN that is known as UNet, with added layers.

With all this, both of these project partners worked hard and created an effective and efficient mobile training module. However, the result of all this hard work and efforts has shown that the proposed architecture of deep learning is way better and superior as compared to CNNs in terms of classifications or clustering of SSSCimages.

Accuracy of the new artificial neural network design

As per Akay, after some fine-tuning of the proposed network, their results have been shown tremendous results. Do you want to know about these results? Well, let’s have a look at the information below to get a better idea about the things in this regard:

• The proposed network architecture reached the accuracy of 100% on their training set of images.
• However, the results showed an accuracy of 96.8% on the validation set of images.
• Even more, the proposed network has also shown an accuracy rate of 95.2% on the testing set of images.
• Most amazingly, the duration to train this network design was less than 5 hours.

Final thoughts

Earlier detection of skin problems is highly critical to avoid any dangerous and deadly situation. However, when it comes to visiting the dermatologist in this regard, then the entire process may delay the detection and treatment of any potential skin disease you are suffering from. However, to provide ease, convenience, and efficiency to both patients and physicians, a new artificial neural network design has been introduced. With this automated solution, people now will be able to screen and diagnose their skin ailments on your laptop without