Deepak Ranjan Nayak

Deepak Ranjan Nayak
Malaviya National Institute of Technology Jaipur · Department of Computer Engineering

Ph.D. Postdoc

About

54
Publications
17,397
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1,246
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Introduction
Deepak Ranjan Nayak currently works at the Department of CSE, Malaviya National Institute of Technology Jaipur, India. Prior to that, he worked at Department of CSE, SVNIT Surat and IIITDM, Kancheepuram, India. Deepak does research in Medical Image Analysis, Machine Learning, Artificial Neural Network and Computer Vision. His current project is 'analysis and classification of medical images using advanced machine learning approaches.'

Publications

Publications (54)
Article
Melanoma is the fastest growing and most lethal cancer among all forms of skin cancer. Deep learning methods, mainly convolutional neural networks (CNNs) have recently brought considerable attention in detecting skin cancers from dermoscopy images. However, learning valuable features by these methods has been challenging due to the inadequate train...
Article
Timely and accurate diagnosis of coronavirus disease 2019 (COVID-19) is crucial in curbing its spread. Slow testing results of reverse transcription-polymerase chain reaction (RT-PCR) and a shortage of test kits have led to consider chest computed tomography (CT) as an alternative screening and diagnostic tool. Many deep learning methods, especiall...
Article
Recent years have witnessed a rise in employing deep learning methods, especially convlolutional neural networks (CNNs) for detection of COVID-19 cases using chest CT scans. Most of the state-of-the-art models demand a huge amount of parameters which often suffer from overfitting in the presence of limited training samples such as chest CT data and...
Conference Paper
Automated skin cancer diagnosis is challenging due to inter-class uniformity, intra-class variation, and the complex structure of dermoscopy images. Convolutional neural networks (CNN) have recently made considerable progress in melanoma classification, even in the presence of limited skin images. One of the drawbacks of these methods is the loss o...
Article
Full-text available
Skin cancer detection through dermoscopy images has remained challenging due to higher inter‐class uniformity and intra‐class diversity. Deep convolutional neural networks (CNNs) have recently obtained remarkable attention for automated skin cancer classification; however, most of these methods extract features from the global image of high resolut...
Article
Full-text available
Aim : COVID-19 is a disease caused by a new strain of coronavirus. Up to 18th October 2020, worldwide there have been 39.6 million confirmed cases resulting in more than 1.1 million deaths. To improve diagnosis, we aimed to design and develop a novel advanced AI system for COVID-19 classification based on chest CT (CCT) images. Methods : Our datas...
Chapter
Deep learning based automated approaches mainly based on convolution neural networks (CNN) has recently brought significant attention to diagnose skin cancers (melanoma) from dermoscopic images. However, learning efficient features from these models has been challenging due to unavailability of ample amount of data. To address this problem, in this...
Article
Full-text available
Cerebral microbleed (CMB) is a type of biomarker, which is related to cerebrovascular diseases. In this paper, a novel computer aided diagnosis method for CMB detection was presented. Firstly, sliding neighbourhood algorithm was used to generate CMB and non-CMB samples from brain susceptibility weighted images. Then, a 15-layer proposed FeatureNet...
Article
Full-text available
Abstract Facial expression recognition has been a long‐standing problem in the field of computer vision. This paper proposes a new simple scheme for effective recognition of facial expressions based on a hybrid feature descriptor and an improved classifier. Inspired by the success of stationary wavelet transform in many computer vision tasks, stati...
Article
Glaucoma is an ocular disorder that affects the optic nerve and ultimately leads to partial or complete vision loss. Hence, there is a strong need for early screening of glaucoma. Earlier diagnosis schemes mostly rely on handcrafted feature engineering. On the other hand, the non-handcrafted feature extraction methods are generally designed with th...
Article
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Ear based identity recognition subject to uncontrolled conditions such as illumination changes, pose variation, low contrast, partial occlusion and noise, is an active research area in the field of biometrics. Meanwhile, multimodal biometrics is becoming increasingly popular and offers improved performance due to the use of multiple sources of info...
Article
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The emergence of Coronavirus Disease 2019 (COVID-19) in early December 2019 has caused immense damage to health and global well-being. Currently, there are approximately five million confirmed cases and the novel virus is still spreading rapidly all over the world. Moreover, many hospitals across the globe are not yet equipped with an adequate amou...
Article
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In recent years, the non-handcrafted feature extraction methods have gained increasing popularity for solving pattern classification tasks due to their inherent ability to extract robust features and handle outliers. However, the design of such features demands a large set of training data. Meta-heuristic optimization schemes can facilitate feature...
Article
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(Aim) To more efficiently diagnose secondary pulmonary tuberculosis, we build an improved convolutional neural network (ICNN) based on recent deep learning technologies. (Method) First, a 12-way data augmentation (DA-12) was proposed to increase size of training set. Second, stochastic pooling was introduced to replace the standard average pooling...
Article
Full-text available
To more efficiently diagnose secondary pulmonary tuberculosis, we build an improved convolutional neural network (ICNN) based on recent deep learning technologies. First, a 12-way data augmentation (DA-12) was proposed to increase size of training set. Second, stochastic pooling was introduced to replace the standard average pooling and max pooling...
Article
Full-text available
Hearing loss (HL) is a kind of common illness, which can significantly reduce the quality of life. For example, HL often results in mishearing, misunderstanding, and communication problems. Therefore, it is necessary to provide early diagnosis and timely treatment for HL. This study investigated the advantages and disadvantages of three classical m...
Article
Automated diagnosis of pathological brain not only reduces the diagnostic error significantly but also improves the patient's quality life, thereby addressing the sustainability issues. The last few decades have witnessed an intensive research on binary classification of brain magnetic resonance (MR) images. Multiclass classification of pathologica...
Article
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Segmentation of handwritten words into isolated characters and their recognition are challenging due to the presence of high variability and cursiveness in Indian scripts. The complex shapes and availability of numerous atomic character classes, compound characters, modifiers, ascendants, and descendants make the recognition task even more difficul...
Article
Full-text available
Automatic binary classification of brain magnetic resonance (MR) images has made remarkable progress in the past decade. In comparison, a few pieces of work has been reported on multiclass classification of brain MR images. However, there exist enough scopes for improved automation and accuracy. Most of the existing schemes follow the multi-stage p...
Article
Automated detection of multi-class brain abnormalities through magnetic resonance imaging (MRI) has received much attention due to its clinical significance and therefore has become an active area of research over the years. The earlier automated schemes often followed traditional machine learning paradigms, in which the proper choice of features a...
Article
Automated diagnosis of two-class brain abnormalities through magnetic resonance imaging (MRI) has progressed significantly in past few years. In contrast, there exists a limited amount of methods proposed to date for multiclass brain abnormalities detection. Such detection has shown its importance in biomedical research and has remained a challengi...
Article
Binary classification of brain magnetic resonance (MR) images has made remarkable progress and many automated systems have been developed in the last decade. Multiclass classification of brain MR images is comparatively more challenging and has great clinical significance. Hence, it has recently become an active area of research in biomedical image...
Article
This paper presents an automated method for detection of pathological brain using magnetic resonance (MR) images. The proposed method suggests to derive features using fast discrete curvelet transform. A combined feature reduction algorithm principal component analysis + linear discriminant analysis (PCA + LDA) is then applied to generate a low-dim...
Preprint
Extreme learning machine (ELM), a randomized learning paradigm for a single hidden layer feed-forward network, has gained significant attention for solving problems in diverse domains due to its faster learning ability. The output weights in ELM are determined by an analytic procedure, while the input weights and biases are randomly generated and f...
Article
Full-text available
Extreme learning machine (ELM), a randomized learning paradigm for single hidden layer feed-forward network, has gained significant attention for solving problems in diverse domains due to its faster learning ability. The output weights in ELM are determined by an analytic procedure, while the input weights and biases are randomly generated and fix...
Article
Sensorineural hearing loss (SNHL) is a common hearing disorder or deafness which accounts for about 90% of the reported hearing loss. Magnetic resonance imaging (MRI) has been found to be an effective neuroimaging technique for detecting SNHL. However, manual detection methods, mainly based on the visual inspection of MRI, are cumbersome, time-cons...
Article
Full-text available
Pathological brain detection systems (PBDSs) have drawn much attention from researchers over the past two decades because of their significance in taking correct clinical decisions. In this paper, an efficient PBDS based on MR images is introduced that markedly improves the recent results. The proposed system makes use of contrast limited adaptive...
Article
Background In the past decades, handwritten character recognition has received considerable attention from researchers across the globe because of its wide range of applications in daily life. From the literature, it has been observed that there is limited study on various handwritten Indian scripts and Odia is one of them. We revised some of the p...
Article
Development of automated diagnosis systems has taken a major place in current research practice to assist medical experts in decision-making. This paper presents a new automatic system for detection of pathological brain through magnetic resonance imaging (MRI). The proposed system involves contrast enhancement of input MR images using contrast lim...
Article
Full-text available
This paper aims at developing an automatic pathological brain detection system (PBDS) to assist radiologists in identifying brain diseases correctly in less time. Magnetic resonance imaging (MRI) has the potential to provide better information about the brain soft tissues and hence MR images have been incorporated in the proposed system. Fifty larg...
Chapter
Optical character recognition (OCR) is one of the most popular and challenging topic of pattern recognition with a wide range of applications in various fields. This paper proposes an OCR system for Odia scripts which comprises of three stages, namely preprocessing, feature extraction, and classification. In the preprocessing stage, we have employe...
Article
Full-text available
Pathological brain detection has made notable stride in the past years, as a consequence many pathological brain detection systems (PBDSs) have been proposed. But, the accuracy of these systems still needs significant improvement in order to meet the necessity of real world diagnostic situations. In this paper, an efficient PBDS based on MR images...
Article
Recently there has been remarkable advances in computer-aided diagnosis (CAD) system development for detection of the pathological brain through MR images. Feature extractors like wavelet and its variants, and classifiers like feed-forward neural network (FNN) and support vector machine (SVM) are very often used in these systems despite the fact th...
Article
Computer-aided diagnosis (CAD) systems have drawn attention of researchers for arriving at qualitative and faster clinical decisions, and hence has become one of the most important directions of research. In this paper, we propose an efficient CAD system to classify pathological and healthy brains using brain MR images. The suggested pathological b...
Article
Full-text available
Aim: Sensorineural hearing loss is correlated to massive neurological or psychiatric disease. Materials: T1-weighted volumetric images were acquired from fourteen subjects with right-sided hearing loss (RHL), fifteen subjects with left-sided hearing loss (LHL), and twenty healthy controls (HC). Method: We treated a three-class classification p...
Article
This paper presents an automatic classification system for segregating pathological brain from normal brains in magnetic resonance imaging scanning. The proposed system employs contrast limited adaptive histogram equalization scheme to enhance the diseased region in brain MR images. Two-dimensional stationary wavelet transform is harnessed to extra...
Article
Design of parallel algorithms for edge detection is extremely important for image analysis and understanding. Cellular automata are the most common and simple models of parallel computation and over the last decade, numerous cellular automata techniques have already been proposed. This paper presents a novel method for edge detection of optical cha...
Conference Paper
Full-text available
Two filters in the light of two-dimensional Cellular Automata (CA) are presented in this paper for salt and pepper noise reduction of an image. The design of a parallel algorithm to remove noise from corrupted images is a demanded approach now, so we utilize the idea of cellular automata to cater this need. The filters are mainly designed according...
Conference Paper
Full-text available
Development of computer-aided diagnosis (CAD) systems for early detection of the pathological brain is essential to save medical resources. In recent years, a variety of techniques have been proposed to upgrade the system’s performance. In this paper, a new automatic CAD system for brain magnetic resonance (MR) image classification is proposed. The...
Conference Paper
Full-text available
Developing automatic and accurate computer-aided diagnosis (CAD) systems for detecting brain disease in magnetic resonance imaging (MRI) are of great importance in recent years. These systems help the radiologists in accurate interpretation of brain MR images and also substantially reduce the time needed for it. In this paper, a new system for abno...
Article
Full-text available
This paper presents an automated and accurate computer-aided diagnosis (CAD) system for brain magnetic resonance (MR) image classification. The system first utilizes two-dimensional discrete wavelet transform (2D DWT) for extracting features from the images. After feature vector normalization, probabilistic principal component analysis (PPCA) is em...
Article
Full-text available
Parallel algorithms for solving any image processing task is a highly demanded approach in the modern world. Cellular Automata (CA) are the most common and simple models of parallel computation. So, CA has been successfully used in the domain of image processing for the last couple of years. This paper provides a survey of available literatures of...
Article
Full-text available
Cellular Automata (CA) are common and most simple models of parallel computations. Edge detection is one of the crucial task in image processing, especially in processing biological and medical images. CA can be successfully applied in image processing. This paper presents a new method for edge detection of binary images based on two dimensional tw...
Article
Full-text available
This paper proposes a new pattern of two dimensional cellular automata linear rules that are used for efficient edge detection of an image. Since cellular automata is inherently parallel in nature, it has produced desired output within a unit time interval. We have observed four linear rules among 512 total linear rules of a rectangular cellular au...
Article
Full-text available
Rainfall prediction is one of the most important and challenging task in the modern world. In general, climate and rainfall are highly non-linear and complicated phenomena, which require advanced computer modeling and simulation for their accurate prediction. An Artificial Neural Network (ANN) can be used to predict the behavior of such nonlinear s...

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Projects (2)
Project
Our goal is to develop a Computer-aided Diagnosis (CAD) system to detect Alzheimer's disease which can help medical personnel to take correct and quick decisions.