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With the changing lifestyle, a large population suffers from a bone disease known as an osteoarthritis affecting the knee, spine, and hip. Therefore, timely detection and classification of the disease are necessary to minimize the loss, however, it is a time-consuming task and requires various tests and physicians’ in-depth analysis. Thus, an accurate automated technique, timely detection and classification are needed to cope with the aforementioned challenges. This study proposes a technique based on an efficient DenseNet that uses the knee image’ features to identify the Knee Osteoarthritis (KOA) and determine its severity level according to the KL grading system such as Grade-I, Grade-II, Grade-III, and Grade-IV. We introduced the reweighted cross-entropy loss function which makes our proposed algorithm more robust as the training data is imbalanced. The dense connections of efficient DenseNet with regularization power help to reduce the overfitting during the training of small knee sample training sets. The proposed algorithm is an efficient approach that can identify the early symptoms of KOA and classify the severity level of the disease for better decision making by orthopedics. The algorithm is a pre-trained network that does not require a huge training set, therefore, the existing dataset i.e. Mendeley VI has been utilized for the training and testing. Additionally, cross-validation has been employed using the OAI dataset to assess the performance of the proposed model. The algorithm achieved 98.22% accuracy over the testing set and 98.08% accuracy over cross-validation. Various experiments have been performed to confirm that our proposed algorithm is more consistent and capable of detecting and classifying the KOA disease than existing state of the art.

Mini Biochip Revolutionizes Disease Detection.

Researchers Closer to Developing Ebola Vaccine.

Gene Pin 1 Could Provide New Alzheimer’s Treatment.

American Researchers Developing SARS Vaccine through Engineering of Cold Virus.

The following topics are under this section:

• Combination Immunotherapy for Hepatocellular Carcinoma
• Sustainability in Healthcare Financing
• MiRXES – A Game Changer in MicroRNA Applications

Heart disease kills more people around the world than any other disease, and it is one of the leading causes of death in the UK, triggering up to 74,000 deaths per year. An essential part in the prevention of deaths by heart disease and thus heart disease itself is the analysis of biomedical markers to determine the risk of a person developing heart disease. Lots of research has been conducted to assess the accuracy of detecting heart disease by analyzing biomedical markers. However, no previous study has attempted to identify the biomedical markers which are most important in this identification. To solve this problem, we proposed a machine learning-based intelligent heart disease prediction system called BioLearner for the determination of vital biomedical markers. This study aims to improve upon the accuracy of predicting heart disease and identify the most essential biological markers. This is done with the intention of composing a set of markers that impacts the development of heart disease the most. Multiple factors determine whether or not a person develops heart disease. These factors are thought to include age, history of chest pain (of different types), fasting blood sugar of different types, heart rate, smoking, and other essential factors. The dataset is analyzed, and the different aspects are compared. Various machine learning models such as $K$ Nearest Neighbours, Neural Networks, Support Vector Machine (SVM) are trained and used to determine the accuracy of our prediction for future heart disease development. BioLearner is able to predict the risk of heart disease with an accuracy of 95%, much higher than the baseline methods.

Due to a few great advantages such as viewing the entire small intestine with almost non-invasiveness and no sedation over traditional endoscopies and other imaging techniques for gastrointestinal tract diseases, the wireless capsule endoscopy invented by Given Imaging has found its gradually wide applications in hospitals. However, one major issue concerning this new technology is that too many images to be examined by naked eyes cause a huge burden to physicians, so it is very necessary to ease the physician if we can do diseases detection using computerized methods. In this paper, we develop a new method by making use of color feature, also a very important clue for diagnosis by physicians, to discriminate between normal region and abnormal region. Exploiting the color histogram of the image, we can get the distribution of the color in the image. Then we use the minimum distance classifier to judge the status of the regions. Experimental results on our present data prove promising performance of the proposed scheme in detecting bleeding and ulcers.

We present the solution of the inverse problem for partially known elastic modulus values, e.g., the elastic modulus is known in some small region on the boundary of the domain from measurements. The inverse problem is posed as a constrained minimization problem and regularized with two different regularization types. In particular, the total variation diminishing (TVD) and the total contrast diminishing (TCD) regularizations are employed. We test both regularization strategies with theoretical diseased tissues, such as a stiff tumor surrounded by healthy background tissue and an atherosclerotic plaque having a soft inclusion surrounded by a stiff cap. In the present study, it is assumed that no traction data is available and the absolute elastic modulus distribution is calibrated from partially known elastic modulus values. We observe that this calibration fails with TVD regularization, while TCD regularization yields well-recovered absolute elastic modulus reconstructions in the presence of high noise levels in the displacement data. Finally, we investigate this problem analytically and provide an explanation for these observations. This work will advance efforts in parameter identification of heterogeneous materials as it provides a methodology to incorporate partially known parameters into the inverse problem formulation that will ultimately drive the inverse solution to an absolute and unique parameter distribution. This has great importance in classifying breast tumors based on their elastic modulus values and in planning surgical interventions of atherosclerotic plaques.

Support vector machine (SVM) is a widely used tool in the field of image processing and pattern recognition. However, the parameters selection of SVMs is a dilemma in disease identification and clinical diagnosis. This paper proposed an improved parameter optimization method based on traditional particle swarm optimization (PSO) algorithm by changing the fitness function in the traditional evolution process of SVMs. Then, this PSO method was combined with simulated annealing global searching algorithm to avoid local convergence that traditional PSO algorithms usually run into. And this method has achieved better results which reflected in the receiver-operating characteristic curves in medical images classification and has gained considerable identification accuracy in clinical disease detection.

Lung cancer is a malicious disorder that affects millions of people around the globe. Premature detection and classification of malignant cells can gradually improve the rate of survival of the patients. The study proposed an innovative strategy for lung cancer identification and classification by utilizing the hybrid deep learning (DL) technique. The presented strategy contains two major parts, segmentation and classification. Attention gated (AG) network is used for segmenting the affected regions. AGs guide a method’s attention to significant regions while repressing the activation of features in non-related fields. This augments the model’s representative influence without significantly increasing computing cost or the number of model parameters. For the lung cancer classification, by combining the architectures of LeNet and Dense Convolutional Network (DenseNet), the proposed LeNet–DenseNet was developed. LeNet and DenseNet are two popular CNN architectures known for the classification of images. LeNet comprises alternating and convolutional layers, followed by fully connected layers. As per the idea of densely connected layers, each layer in DenseNet, which is a recent CNN architecture, is associated with other layers in a feed-forward fashion. This allows for better feature reuse and gradient flow and lessens the parameter count. The findings stated that the proposed method outperforms when compared to other traditional strategies corresponding to specificity, sensitivity and accuracy. The presented approach assists in early-stage detection of high-accuracy lung cancer, which is considered crucial for the treatment procedures in time. The findings stipulated that the presented LeNet–DenseNet model procured accuracy of 94.8%, sensitivity of 96.8% and specificity of 93.5%.

In this chapter, information technology of the analysis of biomedical diagnostic images, containing dendritic structures, in particular, images of fundus blood vessels, and crystallograms of biological liquids is considered. The mathematical apparatus is presented for estimation of numerical characteristics (diagnostic features) on the basis of the geometrical parameters estimation of the medical diagnostic images, allowing to automate diagnostic researches and to raise reliability of medical diagnostics. Mathematical models of objects with correspondence to various classes of diagnostic images are developed such as dendritic structures. The mathematical methods used in the chapter include the method of local fan-beam transformation, the textural analysis, the wavelet analysis and the discriminant analysis. Experimental researches are presented upon modeled images as well as upon natural diagnostic images.

Critical care medicine(CCM) covers all aspects of acute and emergency care for critical illness. While the study of disease detection is pervasive in medical disciplines, there is little study specifically in CCM. In this paper, we propose a novel CCM disease-detection method based on the anti-interference decision tree, which distinguishes target symptoms and lab indexes from irrelevant ones as well as provide the decision paths. In this paper, we use the first filter to eliminate irrelevant records of the original medical records. Using the second filter we remove the useless parts of medical records. The remaining parts, lab indexes and symptoms are identified as our training source for constructing the antiinterference decision tree based on the C4.5 algorithm. The top N probable decision paths on the tree are provided to help diagnosis. The anti-interference ability of the method is demonstrated through comparisons in real data of CCM. The experiments conducted indicate that the proposed method has anti-interference ability and attains high detection accuracy in nosocomial infection.