Biomedical image classification based on a cascade of an SVM with a reject option and subspace analysis

Fisheye transformation enhances deep-learning-based single-cell phenotyping by including cellular microenvironment

Incorporating information about the surroundings can have a significant impact on successfully determining the class of an object. This is of particular interest when determining the phenotypes of cells, for example, in the context of high-throughput screens. We hypothesized that an ideal approach would consider the fully featured view of the cell of interest, include its neighboring microenvironment, and give lesser weight to cells that are far from the cell of interest. To satisfy these criteria, we present an approach with a transformation similar to those characteristic of fisheye cameras. Using this transformation with proper settings, we could significantly increase the accuracy of single-cell phenotyping, both in the case of cell culture and tissue-based microscopy images, and we present improved results on a dataset containing images of wild animals.

PSO optimized 1-D CNN-SVM architecture for real-time detection and classification applications

In this paper, we propose a novel Particle Swarm Optimized (PSO) One-Dimensional Convolutional Neural Network with Support Vector Machine (1-D CNN-SVM) architecture for real-time detection and classification of diseases. The performance of the proposed architecture is validated with a novel hardware model for detecting Chronic Kidney Disease (CKD) from saliva samples. For detecting CKD, the urea concentration in the saliva sample is monitored by converting it into ammonia. The urea on hydrolysis in the presence of urease enzyme produces ammonia. This ammonia is then measured using a semiconductor gas sensor. The sensor response is given to the proposed architecture for feature extraction and classification. The performance of the architecture is optimized by regulating the parameter values using a PSO algorithm. The proposed architecture outperforms current conventional methods, as this approach is a combination of strong feature extraction and classification techniques. Optimal features are extracted directly from the raw signal, aiming to reduce the computational time and complexity. The proposed architecture has achieved an accuracy of 98.25%.

An Automatic Classification Method on Chronic Venous Insufficiency Images

Chronic venous insufficiency (CVI) affect a large population, and it cannot heal without doctors' interventions. However, many patients do not get the medical advisory service in time. At the same time, the doctors also need an assistant tool to classify the patients according to the severity level of CVI. We propose an automatic classification method, named CVI-classifier to help doctors and patients. In this approach, first, low-level image features are mapped into middle-level semantic features by a concept classifier, and a multi-scale semantic model is constructed to form the image representation with rich semantics. Second, a scene classifier is trained using an optimized feature subset calculated by the high-order dependency based feature selection approach, and is used to estimate CVI's severity. At last, classification accuracy, kappa coefficient, F1-score are used to evaluate classification performance. Experiments on the CVI images from 217 patients' medical records demonstrated superior performance and efficiency for CVI-classifier, with classification accuracy up to 90.92%, kappa coefficient of 0.8735 and F1score of 0.9006. This method also outperformed doctors' diagnosis (doctors rely solely on images to make judgments) with accuracy, kappa and F1-score improved by 9.11%, 0.1250 and 0.0955 respectively.