Fog Computing Employed Computer Aided Cancer Classification System Using Deep Neural Network in Internet of Things Based Healthcare System.

Computer assisted automatic smart pattern analysis of cancer affected pixel structure takes critical role in pre-interventional decision making for oral cancer treatment. Internet of Things (IoT) in healthcare systems is now emerging solution for modern e-healthcare system to provide high quality medical care. In this research work, we proposed a novel method which utilizes a modified vesselness measurement and a Deep Convolutional Neural Network (DCNN) to identify the oral cancer region structure in IoT based smart healthcare system. The robust vesselness filtering scheme handles noise while reserving small structures, while the CNN framework considerably improves classification accuracy by deblurring focused region of interest (ROI) through integrating with multi-dimensional information from feature vector selection step. The marked feature vector points are extracted from each connected component in the region and used as input for training the CNN. During classification, each connected part is individually analysed using the trained DCNN by considering the feature vector values that belong to its region. For a training of 1500 image dataset, an accuracy of 96.8% and sensitivity of 92% is obtained. Hence, the results of this work validate that the proposed algorithm is effective and accurate in terms of classification of oral cancer region in accurate decision making. The developed system can be used in IoT based diagnosis in health care systems, where accuracy and real time diagnosis are essential.

An Internet of Things based physiological signal monitoring and receiving system for virtual enhanced health care network.

BACKGROUND: Wireless physiological signal monitoring system designing with secured data communication in the health care system is an important and dynamic process. OBJECTIVE: We propose a signal monitoring system using NI myRIO connected with the wireless body sensor network through multi-channel signal acquisition method. Based on the server side validation of the signal, the data connected to the local server is updated in the cloud. The Internet of Things (IoT) architecture is used to get the mobility and fast access of patient data to healthcare service providers. METHODS: This research work proposes a novel architecture for wireless physiological signal monitoring system using ubiquitous healthcare services by virtual Internet of Things. RESULTS: We showed an improvement in method of access and real time dynamic monitoring of physiological signal of this remote monitoring system using virtual Internet of thing approach. This remote monitoring and access system is evaluated in conventional value. This proposed system is envisioned to modern smart health care system by high utility and user friendly in clinical applications. CONCLUSION: We claim that the proposed scheme significantly improves the accuracy of the remote monitoring system compared to the other wireless communication methods in clinical system.

Evolutionary computing based approach for the removal of ECG artifact from the corrupted EEG signal.

BACKGROUND: Electroencephalogram (EEG) is an important tool for clinical diagnosis of brain-related disorders and problems. However, it is corrupted by various biological artifacts, of which ECG is one among them that reduces the clinical importance of EEG especially for epileptic patients and patients with short neck. OBJECTIVE: To remove the ECG artifact from the measured EEG signal using an evolutionary computing approach based on the concept of Hybrid Adaptive Neuro-Fuzzy Inference System, which helps the Neurologists in the diagnosis and follow-up of encephalopathy. METHODS: The proposed hybrid learning methods are ANFIS-MA and ANFIS-GA, which uses Memetic Algorithm (MA) and Genetic algorithm (GA) for tuning the antecedent and consequent part of the ANFIS structure individually. The performances of the proposed methods are compared with that of ANFIS and adaptive Recursive Least Squares (RLS) filtering algorithm. RESULTS: The proposed methods are experimentally validated by applying it to the simulated data sets, subjected to non-linearity condition and real polysomonograph data sets. Performance metrics such as sensitivity, specificity and accuracy of the proposed method ANFIS-MA, in terms of correction rate are found to be 93.8%, 100% and 99% respectively, which is better than current state-of-the-art approaches. CONCLUSIONS: The evaluation process used and demonstrated effectiveness of the proposed method proves that ANFIS-MA is more effective in suppressing ECG artifacts from the corrupted EEG signals than ANFIS-GA, ANFIS and RLS algorithm.

An intelligent framework for medical image retrieval using MDCT and multi SVM.

BACKGROUND: Volumes of medical images are rapidly generated in medical field and to manage them effectively has become a great challenge. This paper studies the development of innovative medical image retrieval based on texture features and accuracy. OBJECTIVE: The objective of the paper is to analyze the image retrieval based on diagnosis of healthcare management systems. METHODS: This paper traces the development of innovative medical image retrieval to estimate both the image texture features and accuracy. The texture features of medical images are extracted using MDCT and multi SVM. Both the theoretical approach and the simulation results revealed interesting observations and they were corroborated using MDCT coefficients and SVM methodology. RESULTS: All attempts to extract the data about the image in response to the query has been computed successfully and perfect image retrieval performance has been obtained. Experimental results on a database of 100 trademark medical images show that an integrated texture feature representation results in 98% of the images being retrieved using MDCT and multi SVM. CONCLUSION: Thus we have studied a multiclassification technique based on SVM which is prior suitable for medical images. The results show the retrieval accuracy of 98%, 99% for different sets of medical images with respect to the class of image.

An automated retinal imaging method for the early diagnosis of diabetic retinopathy.

BACKGROUND: Diabetic retinopathy is a microvascular complication of long-term diabetes and is the major cause for eyesight loss due to changes in blood vessels of the retina. Major vision loss due to diabetic retinopathy is highly preventable with regular screening and timely intervention at the earlier stages. Retinal blood vessel segmentation methods help to identify the successive stages of such sight threatening diseases like diabetes. OBJECTIVE: To develop and test a novel retinal imaging method which segments the blood vessels automatically from retinal images, which helps the ophthalmologists in the diagnosis and follow-up of diabetic retinopathy. METHODS: This method segments each image pixel as vessel or nonvessel, which in turn, used for automatic recognition of the vasculature in retinal images. Retinal blood vessels were identified by means of a multilayer perceptron neural network, for which the inputs were derived from the Gabor and moment invariants-based features. Back propagation algorithm, which provides an efficient technique to change the weights in a feed forward network, is utilized in our method. RESULTS: Quantitative results of sensitivity, specificity and predictive values were obtained in our method and the measured accuracy of our segmentation algorithm was 95.3%, which is better than that presented by state-of-the-art approaches. CONCLUSIONS: The evaluation procedure used and the demonstrated effectiveness of our automated retinal imaging method proves itself as the most powerful tool to diagnose diabetic retinopathy in the earlier stages.