Rekh Ram Janghel

Bio: Rekh Ram Janghel is an academic researcher from National Institute of Technology, Raipur. The author has contributed to research in topics: Deep learning & Computer science. The author has an hindex of 12, co-authored 66 publications receiving 472 citations. Previous affiliations of Rekh Ram Janghel include Indian Institutes of Information Technology & Indian Institute of Information Technology and Management, Gwalior.

Deep Convolution Neural Network Based System for Early Diagnosis of Alzheimer's Disease

Abstract: Objectives Alzheimer's Disease (AD) is the most general type of dementia. In all leading countries, it is one of the primary reasons of death in senior citizens. Currently, it is diagnosed by calculating the MSME score and by the manual study of MRI Scan. Also, different machine learning methods are utilized for automatic diagnosis but existing has some limitations in terms of accuracy. So, main objective of this paper to include a preprocessing method before CNN model to increase the accuracy of classification. Materials and method In this paper, we present a deep learning-based approach for detection of Alzheimer's Disease from ADNI database of Alzheimer's disease patients, the dataset contains fMRI and PET images of Alzheimer's patients along with normal person's image. We have applied 3D to 2D conversion and resizing of images before applying VGG-16 architecture of Convolution neural network for feature extraction. Finally, for classification SVM, Linear Discriminate, K means clustering, and Decision tree classifiers are used. Results The experimental result shows that the average accuracy of 99.95% is achieved for the classification of the fMRI dataset, while the average accuracy of 73.46% is achieved with the PET dataset. On comparing results on the basis of accuracy, specificity, sensitivity and on some other parameters we found that these results are better than existing methods. Conclusions this paper, suggested a unique way to increase the performance of CNN models by applying some preprocessing on image dataset before sending to CNN architecture for feature extraction. We applied this method on ADNI database and on comparing the accuracies with other similar approaches it shows better results.

Recent Deep Learning Techniques, Challenges and Its Applications for Medical Healthcare System: A Review

Abstract: The concept of deep learning originates from artificial neural networks which has become a very popular research area during the past few decades. There are two main reasons for for the wide acceptance of deep learning. First one being the overfitting problem has been partially resolved with the advent of big data analytics techniques. The second point for wide acceptance of deep learning is that deep neural networks undergo pre-training procedure before unsupervised learning, which assigns some initial values to the network. This article describes the all deep learning techniques and their experimental analysis with advantage and disadvantages. This review highlights the till date progress of the six deep learning techniques namely, autoencoder, restricted Boltzmann machine, deep belief network, recurrent neural network, convolutional neural network, and generative adversarial network with practical variant case studies. A wide discourse has been taken into consideration for the survey in the article. It concludes try to reflect some of the most fundamental and recent applications in the medical health-care system, and also identify some of the challenges and opportunities of the deep learning techniques.

Breast cancer diagnosis using Artificial Neural Network models

Abstract: Breast cancer is the second leading cause of cancer deaths worldwide and occurrs in one out of eight women. In this paper we develop a system for diagnosis, prognosis and prediction of breast cancer using Artificial Neural Network (ANN) models. This will assist the doctors in diagnosis of the disease. We implement four models of neural networks namely Back Propagation Algorithm, Radial Basis Function Networks, Learning vector Quantization and Competitive Learning Network Experimental results show that Learning Vector Quantization shows the best performance in the testing data set This is followed in order by CL, MLP and RBFN The high accuracy of the LVQ against the other models indicates its better ability for solving the classificatory problem of Breast Cancer diagnosis.

Diagnosis of Thyroid Disorders using Artificial Neural Networks

Abstract: A major problem in medical science is attaining the correct diagnosis of disease in precedence of its treatment. This paper presents the diagnosis of thyroid disorders using Artificial Neural Networks (ANNs). The feed-forward neural network has been trained using three ANN algorithms; the Back propagation algorithm (BPA), the Radial Basis Function (RBF) Networks and the Learning Vector Quantization (LVQ) Networks. The networks are simulated using MATLAB and their performance is assessed in terms of factors like accuracy of diagnosis and training time. The performance comparison helps to find out the best model for diagnosis of thyroid disorders.

Artificial neural networks

Abstract: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Bioelectrical Signal Processing In Cardiac And Neurological Applications

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A Review on Deep Learning Methods for ECG Arrhythmia Classification

Abstract: Deep Learning (DL) has recently become a topic of study in different applications including healthcare, in which timely detection of anomalies on Electrocardiogram (ECG) can play a vital role in patient monitoring. This paper presents a comprehensive review study on the recent DL methods applied to the ECG signal for the classification purposes. This study considers various types of the DL methods such as Convolutional Neural Network (CNN), Deep Belief Network (DBN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). From the 75 studies reported within 2017 and 2018, CNN is dominantly observed as the suitable technique for feature extraction, seen in 52% of the studies. DL methods showed high accuracy in correct classification of Atrial Fibrillation (AF) (100%), Supraventricular Ectopic Beats (SVEB) (99.8%), and Ventricular Ectopic Beats (VEB) (99.7%) using the GRU/LSTM, CNN, and LSTM, respectively.

HIFCF: An effective hybrid model between picture fuzzy clustering and intuitionistic fuzzy recommender systems for medical diagnosis

Abstract: The health care support system is a special type of recommender systems that play an important role in medical sciences nowadays. This kind of systems often provides the medical diagnosis function based on the historic clinical symptoms of patients to give a list of possible diseases accompanied with the membership values. The most acquiring disease from that list is then determined by clinicians’ experience expressed through a specific defuzzification method. An important issue in the health care support system is increasing the accuracy of the medical diagnosis function that involves the cooperation of fuzzy systems and recommender systems in the sense that uncertain behaviors of symptoms and the clinicians’ experience are represented by fuzzy memberships whilst the determination of the possible diseases is conducted by the prediction capability of recommender systems. Intuitionistic fuzzy recommender systems (IFRS) are such the combination, which results in better accuracy of prediction than the relevant methods constructed on either the traditional fuzzy sets or recommender system only. Based upon the observation that the calculation of similarity in IFRS could be enhanced by the integration with the information of possibility of patients belonging to clusters specified by a fuzzy clustering method, in this paper we propose a novel hybrid model between picture fuzzy clustering and intuitionistic fuzzy recommender systems for medical diagnosis so-called HIFCF (Hybrid Intuitionistic Fuzzy Collaborative Filtering). Experimental results reveal that HIFCF obtains better accuracy than IFCF and the standalone methods of intuitionistic fuzzy sets such as De, Biswas & Roy, Szmidt & Kacprzyk, Samuel & Balamurugan and recommender systems, e.g. Davis et al. and Hassan & Syed. The significance and impact of the new method contribute not only the theoretical aspects of recommender systems but also the applicable roles to the health care support systems.