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Pioneering Artificial Intelligence (AI) technology, developed by experts at University of the West of Scotland (UWS), is capable of accurately diagnosing Covid-19 in just a few minutes. The groundbreaking programme is able to detect the virus far more quickly than a PCR test; which typically takes around 2-hours. It is hoped that the technology can eventually be used to help relieve strain on hard-pressed Accident and Emergency departments, particularly in countries where PCR tests are not readily available. The state-of-the-art technique utilizes x-ray technology, comparing scans to a database of around 3000 images, belonging to patients with Covid-19, healthy individuals and people with viral pneumonia. It then uses an AI process known as deep convolutional neural network, an algorithm typically used to analyse visual imagery, to make a diagnosis.
Melanoma is a skin disease with a high fatality rate. Early diagnosis of melanoma can effectively increase the survival rate of patients. There are three types of dermoscopy images, malignant melanoma, benign nevis, and seborrheic keratosis, so using dermoscopy images to classify melanoma is an indispensable task in diagnosis. However, early melanoma classification works can only use the low-level information of images, so the melanoma cannot be classified efficiently; the recent deep learning methods mainly depend on a single network, although it can extract high-level features, the poor scale and type of the features limited the results of the classification. Therefore, we need an automatic classification method for melanoma, which can make full use of the rich and deep feature information of images for classification. In this study, we propose an ensemble method that can integrate different types of classification networks for melanoma classification. Specifically, we first use U-net to segment the lesion area of images to generate a lesion mask, thus resize images to focus on the lesion; then, we use five excellent classification models to classify dermoscopy images, and adding squeeze-excitation block (SE block) to models to emphasize the more informative features; finally, we use our proposed new ensemble network to integrate five different classification results. The experimental results prove the validity of our results. We test our method on the ISIC 2017 challeng...
The eyes may offer a "window into the soul," as poets say, but they also have a lot to say about your health. Dry eyes can be a sign of rheumatoid arthritis. High levels of cholesterol can cause a white, gray or blue ring to form around the colored part of your eye, called the iris. A coppery gold ring circling the iris is a key sign of Wilson's disease, a rare genetic disorder that causes copper to build up in the brain, liver and other organs, slowing poisoning the body. And that's not all: Damage to blood vessels in the back of your eye, called the retina, can be early signs of nerve damage due to diabetes, high blood pressure, coronary artery disease, even cancer, as well as glaucoma and age-related macular degeneration.
Brain tumors analysis is important in timely diagnosis and effective treatment to cure patients. Tumor analysis is challenging because of tumor morphology like size, location, texture, and heteromorphic appearance in the medical images. In this regard, a novel two-phase deep learning-based framework is proposed to detect and categorize brain tumors in magnetic resonance images (MRIs). In the first phase, a novel deep boosted features and ensemble classifiers (DBF-EC) scheme is proposed to detect tumor MRI images from healthy individuals effectively. The deep boosted feature space is achieved through the customized and well-performing deep convolutional neural networks (CNNs), and consequently, fed into the ensemble of machine learning (ML) classifiers.
Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. Some cognitive capabilities of the human brain are inspiring deep learning research.
In a video generation system using Action Prediction (Figure 3), if you drop a drop a ball, the AI should predict the ball will fall on the floor because the implicit knowledges "gates" the model to generate a future where the ball goes upward like a Helium balloon. But how to build such "gating mechanism" (seen in Figure 4) that uses cognitive knowledge of cause & effect? Is it possible to design a futuristic AI that can be trusted by a doctor for a AI diagnosis inspite of sample from a out of distribution? Will mere selective prediction suffix or a deeper understanding of the semantic context and understanding of human anatomy activate a medical decision. During medical diagnosis, how can an AI avoid a false prediction based on conceptual knowledge of the medical domain?
Breast cancer is the most commonly occurring cancer in women and the second most common cancer overall. There were over 2.3 million new cases in 2020, making it a significant health problem in the present day. The key challenge in breast cancer detection is to classify tumors as malignant or benign. Malignant refers to cancer cells that can invade and kill nearby tissue and spread to other parts of your body. Unlike cancerous tumors (malignant), Benign does not spread to other parts of the body and is safe somehow.
A strong spasm, or abrupt contraction, of a coronary artery, which can block blood flow to the heart muscle, is a less common reason. As a result of improvements in machine learning and artificial intelligence, it is now possible to detect and diagnose diabetes in its early stages using an automated procedure that is more efficient than manual diagnosis. Based on autonomous comparison with a huge collection of typical fundus photos, the server uses IDx-DR software and a "deep-learning" algorithm to discover retinal abnormalities compatible with DR. One of two outcomes is provided by the software: (1) Refer to an eyecare professional (ECP) if more than moderate DR is discovered; (2) If the results are negative for more than mild DR, rescreen in 12 months. Machine learning algorithms and their ability to synthesize extremely complex data may open up new avenues for tailoring drugs to a person's genetic composition.
Scientists from Skoltech's iMolecule group have created an artificial intelligence-driven approach to identify sites on the structures of DNA or RNA molecules where drug compounds may bind. The drug-binding site information will allow pharmaceutical firms to find novel therapeutic compounds – including antiviral agents – in a far more focused manner. The new method, which was published in Nucleic Acid Research: Genomics and Bioinformatics, claims to be more accurate than previous methods since it considers how a nucleic acid molecule's shape impacts which binding sites are accessible. Most drugs target proteins because pharmacologists have traditionally seen RNA as just a mediator between DNA and the functional proteins it encodes. As almost 85% of the genome is translated into RNAs, only a tiny percentage of those RNAs encode proteins.