Coronavirus Disease (COVID19) is a fast-spreading infectious disease that is currently causing a healthcare crisis around the world. Due to the current limitations of the reverse transcription-polymerase chain reaction (RT-PCR) based tests for detecting COVID19, recently radiology imaging based ideas have been proposed by various works. In this work, various Deep CNN based approaches are explored for detecting the presence of COVID19 from chest CT images. A decision fusion based approach is also proposed, which combines predictions from multiple individual models, to produce a final prediction. Experimental results show that the proposed decision fusion based approach is able to achieve above 86% results across all the performance metrics under consideration, with average AUROC and F1-Score being 0.883 and 0.867, respectively. The experimental observations suggest the potential applicability of such Deep CNN based approach in real diagnostic scenarios, which could be of very high utility in terms of achieving fast testing for COVID19.
Prediction of breast tumour malignancy using ultrasound imaging, is an important step for early detection of breast cancer. An efficient prediction system can be a great help to improve the survival chances of the involved patients. In this work, a machine learning (ML)—radiomics based classification pipeline is proposed, to perform this predictive modelling task, in a much more efficient manner. Multiple different types of image features of the region of interests are considered in this work, followed by a recursive feature elimination based feature selection step. Furthermore, a synthetic minority oversampling technique based step is also included in the pipeline, to deal with the class imbalance problem, that is often present in medical imaging datasets. Various ML models are considered in the subsequent model training phase, on a publicly available breast ultrasound image dataset (BUSI). From experimental analysis it has been observed that, shape, texture and histogram oriented gradients related features are the most informative, with respect to the predictive modelling task. Furthermore, it was observed that ensemble learners such as random forest, gradient boosting and AdaBoost classifiers are able to achieve significant results with respect to multiple evaluation metrics. The proposed approach achieved the state‐of‐the‐art accuracy, area under the curve, F1‐score and Mathews correlation coefficient values of 0.974, 0.97, 0.94 and 0.959, respectively, on the BUSI dataset. Such kind of impressive results suggest that the proposed approach can have a very high practical utility, in real medical diagnostic settings.
Summary
Among all diabetic‐related complications, the diabetic foot ulcer (DFU) is severe, demanding serious attention, and timely treatment. The purpose of the present study was to conduct features fusion of machine learning (ML) based handcrafted low‐level and convolutional neural networks (CNNs) based high‐level features for improving automatic diagnosis of DFU. A standard image dataset containing 1038 abnormal (DFU) and 641 normal color skin patches has been used for experimental evaluation. Handcrafted features are extracted to uncover the edge, color, shape, and texture information from the images. Also, a new CNN architecture has been proposed based on deeper residual blocks for extracting high‐level features. Features fusion with several ML classifiers like logistic regression classifier, support vector machine, gradient boosting, and artificial neural network have shown improved DFU identification results compared to the individual feature categories. Thereby, LRC outperformed the state‐of‐the‐art results for all evaluation metrics by achieving 95.23% sensitivity, 95.37% F1‐score, and 96.50% area under the curve (AUC) value. Such impressive experimental observations suggest that the proposed approach is expected to provide efficient decision support to medical practitioners, thereby improving patient care.
In the field of Human Computer Interaction (HCI) lot of research has been done. One of the system sign language recognition give a solution for HCI. The system which we design called sign language recognition system. This gives the solution to build the Human Computer Interaction (HCI) where the computer is used as interpreters. These systems are used to recognize the real time static & dynamic sign convention by using MATLAB. Sign are captured using web camera & PCA technique is used for feature extraction. This proposed system can use for real time application due to the use of simple logic condition applied to recognize the sign.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.