Faculty Publications
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Item Recent advances and future potential of computer aided diagnosis of liver cancer on computed tomography images(2011) Arakeri, M.P.; Guddeti, G.Liver cancer has been known as one of the deadliest diseases. It has become a major health issue in the world over the past 30 years and its occurrence has increased in the recent years. Early detection is necessary to diagnose and cure liver cancer. Advances in medical imaging and image processing techniques have greatly enhanced interpretation of medical images. Computer aided diagnosis (CAD) systems based on these techniques play a vital role in the early detection of liver cancer and hence reduce death rate. The concept of computer aided diagnosis is to provide a computer output as a second opinion in analysis of liver cancer. It assists radiologist's image interpretation by improving accuracy and consistency of radiological diagnosis and also by reducing image analysis time. The main objective of this paper is to provide an overview of recent advances in the development of CAD systems for analysis of liver cancer. Medical imaging system based on computer tomography will be focused as it is particularly suitable for detecting liver tumors. The paper begins with introduction to liver tumors and medical imaging techniques. Then the key CAD techniques developed recently for liver tumor detection, classification, case-based reasoning based on image retrieval and 3D reconstruction are presented. This article also explores the future key directions and highlights the research challenges that need to be addressed in the development of CAD system which can help the radiologist in improving the diagnostic accuracy. © Springer-Verlag 2011.Item Fault diagnosis of helical gear box using decision tree through vibration signals(RAMS Consultants, 2013) Sugumaran, V.; Jain, D.; Amarnath, M.; Kumar, H.This paper uses vibration signals acquired from gears in good and simulated faulty conditions for the purpose of fault diagnosis through machine learning approach. The descriptive statistical features were extracted from vibration signals and the important ones were selected using decision tree (dimensionality reduction). The selected features were then used for classification using J48 decision tree algorithm. The paper also discusses the effect of various parameters on classification accuracy. © RAMS Consultants.Item Computer-aided diagnosis system for tissue characterization of brain tumor on magnetic resonance images(Springer London, 2015) Arakeri, M.P.; Guddeti, G.R.M.The manual analysis of brain tumor on magnetic resonance (MR) images is time-consuming and subjective. Thus, to avoid human errors in brain tumor diagnosis, this paper presents an automatic and accurate computer-aided diagnosis (CAD) system based on ensemble classifier for the characterization of brain tumors on MR images as benign or malignant. Brain tumor tissue was automatically extracted from MR images by the proposed segmentation technique. A tumor is represented by extracting its texture, shape, and boundary features. The most significant features are selected by using information gain-based feature ranking and independent component analysis techniques. Next, these features are used to train the ensemble classifier consisting of support vector machine, artificial neural network, and k-nearest neighbor classifiers to characterize the tumor. Experiments were carried out on a dataset consisting of T1-weighted post-contrast and T2-weighted MR images of 550 patients. The developed CAD system was tested using the leave-one-out method. The experimental results showed that the proposed segmentation technique achieves good agreement with the gold standard and the ensemble classifier is highly effective in the diagnosis of brain tumor with an accuracy of 99.09 % (sensitivity 100 % and specificity 98.21 %). Thus, the proposed system can assist radiologists in an accurate diagnosis of brain tumors. © 2013, Springer-Verlag London.Item Engine gearbox fault diagnosis using empirical mode decomposition method and Naïve Bayes algorithm(Springer India sanjiv.goswami@springer.co.in, 2017) Vernekar, K.; Kumar, H.; Gangadharan, K.V.This paper presents engine gearbox fault diagnosis based on empirical mode decomposition (EMD) and Naïve Bayes algorithm. In this study, vibration signals from a gear box are acquired with healthy and different simulated faulty conditions of gear and bearing. The vibration signals are decomposed into a finite number of intrinsic mode functions using the EMD method. Decision tree technique (J48 algorithm) is used for important feature selection out of extracted features. Naïve Bayes algorithm is applied as a fault classifier to know the status of an engine. The experimental result (classification accuracy 98.88%) demonstrates that the proposed approach is an effective method for engine fault diagnosis. © 2017, Indian Academy of Sciences.Item Condition monitoring of single point cutting tools based on machine learning approach(International Institute of Acoustics and Vibrations P O Box 13 Auburn AL 36831, 2018) Gangadhar, N.; Kumar, H.; Narendranath, S.; Sugumaran, V.This paper presents the use of multilayer perceptron (MLP) for fault diagnosis through a histogram feature extracted from vibration signals of healthy and faulty conditions of single point cutting tools. The features were extracted from the vibration signals, which were acquired while machining with healthy and different worn-out tool conditions. Principle component analysis (PCA) used to select important extracted features. The artificial neural network (ANN) algorithm was applied as a fault classifier in order to know the status of cutting tool conditions. The accuracy of classification with MLP was found to be 82.5 %, which validates that the proposed approach is an effective method for fault diagnosis of single point cutting tools. © 2018 International Institute of Acoustics and Vibrations. All Rights Reserved.Item Engine gearbox fault diagnosis using machine learning approach(Emerald Group Publishing Ltd. Howard House Wagon Lane, Bingley BD16 1WA, 2018) Vernekar, K.; Kumar, H.; Gangadharan, K.V.Purpose: Bearings and gears are major components in any rotatory machines and, thus, gained interest for condition monitoring. The failure of such critical components may cause an increase in down time and maintenance cost. Condition monitoring using the machine learning approach is a conceivable solution for the problem raised during the operation of the machinery system. The paper aims to discuss these issues. Design/methodology/approach: This paper aims engine gearbox fault diagnosis based on a decision tree and artificial neural network algorithm. Findings: The experimental result (classification accuracy 85.55 percent) validates that the proposed approach is an effective method for engine gearbox fault diagnosis. Originality/value: This paper attempts to diagnose the faults in engine gearbox based on the machine learning approach with the combination of statistical features of vibration signals, decision tree and multi-layer perceptron neural network techniques. © 2018, Emerald Publishing Limited.Item Novel color normalization method for hematoxylin eosin stained histopathology images(Institute of Electrical and Electronics Engineers Inc., 2019) Roy, S.; Lal, S.; Kini, J.R.With the advent of computer-assisted diagnosis (CAD), the accuracy of cancer detection from histopathology images is significantly increased. However, color variation in the CAD system is inevitable due to the variability of stain concentration and manual tissue sectioning. The small variation in color may lead to the misclassification of cancer cells. Therefore, color normalization is a very much essential step prior to segmentation and classification in order to reduce the inter-variability of background color among a set of source images. In this paper, a novel color normalization method is proposed for Hematoxylin and Eosin stained histopathology images. Conventional Reinhard algorithm is modified in our proposed method by incorporating fuzzy logic. Moreover, mathematically, it is proved that our proposed method satisfies all three hypotheses of color normalization. Furthermore, several quality metrics are estimated locally for evaluating the performance of various color normalization methods. The experimental result reveals that our proposed method has outperformed all other benchmark methods. © 2019 IEEE.Item NucleiSegNet: Robust deep learning architecture for the nuclei segmentation of liver cancer histopathology images(Elsevier Ltd, 2021) Lal, S.; Das, D.; Alabhya, K.; Kanfade, A.; Kumar, A.; Kini, J.R.The nuclei segmentation of hematoxylin and eosin (H&E) stained histopathology images is an important prerequisite in designing a computer-aided diagnostics (CAD) system for cancer diagnosis and prognosis. Automated nuclei segmentation methods enable the qualitative and quantitative analysis of tens of thousands of nuclei within H&E stained histopathology images. However, a major challenge during nuclei segmentation is the segmentation of variable sized, touching nuclei. To address this challenge, we present NucleiSegNet - a robust deep learning network architecture for the nuclei segmentation of H&E stained liver cancer histopathology images. Our proposed architecture includes three blocks: a robust residual block, a bottleneck block, and an attention decoder block. The robust residual block is a newly proposed block for the efficient extraction of high-level semantic maps. The attention decoder block uses a new attention mechanism for efficient object localization, and it improves the proposed architecture's performance by reducing false positives. When applied to nuclei segmentation tasks, the proposed deep-learning architecture yielded superior results compared to state-of-the-art nuclei segmentation methods. We applied our proposed deep learning architecture for nuclei segmentation to a set of H&E stained histopathology images from two datasets, and our comprehensive results show that our proposed architecture outperforms state-of-the-art methods. As part of this work, we also introduced a new liver dataset (KMC liver dataset) of H&E stained liver cancer histopathology image tiles, containing 80 images with annotated nuclei procured from Kasturba Medical College (KMC), Mangalore, Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India. The proposed model's source code is available at https://github.com/shyamfec/NucleiSegNet. © 2020 Elsevier LtdItem Segmentation of focal cortical dysplasia lesions from magnetic resonance images using 3D convolutional neural networks(Elsevier Ltd, 2021) Niyas, S.; Chethana Vaisali, S.; Show, I.; Chandrika, T.G.; Vinayagamani, S.; Kesavadas, C.; Rajan, J.Computer-aided diagnosis using advanced Artific ial Intelligence (AI) techniques has become much popular over the last few years. This work automates the segmentation of Focal Cortical Dysplasia (FCD) lesions from three-dimensional (3D) Magnetic Resonance (MR) images. FCD is a type of neuronal malformation in the brain cortex and is the leading cause of intractable epilepsy, irrespective of gender or age differences. Since the neuron related abnormalities are usually resistant to drug therapy, surgical resection has been the main treatment approach for patients with intractable epilepsy. Automating the identification and segmentation of FCD is useful for neuroradiologists in pre-surgical evaluations. Convolutional Neural Networks (CNNs) have the ability to learn appropriate features from the training data without any human intervention. But, most of the state-of-the-art FCD segmentation approaches use two-dimensional (2D) CNN models despite the availability of 3D Magnetic resonance imaging (MRI) volumes, and hence fail to leverage the inter-slice information present in the MRI volumes. The major hurdles in considering a 3D CNN model are the need for a large 3D dataset, big memory, and high computation cost. A deep 3D CNN segmentation model, which can extract inter-slice information and overcomes the drawbacks of conventional 3D CNN methods to an extent, is proposed in this paper. The model uses a 3D version of U-Net with residual blocks that works on shallow depth 3D sub-volumes generated from MRI volumes. The proposed method shows superior performance over the state-of-the-art FCD segmentation methods in both qualitative and quantitative analysis. © 2021 Elsevier LtdItem A novel receptive field-regularized V-net and nodule classification network for lung nodule detection(John Wiley and Sons Inc, 2022) Dodia, S.; Annappa, B.; Mahesh, M.Recent advancements in deep learning have achieved great success in building a reliable computer-aided diagnosis (CAD) system. In this work, a novel deep-learning architecture, named receptive field regularized V-net (RFR V-Net), is proposed for detecting lung cancer nodules with reduced false positives (FP). The method uses a receptive regularization on the encoder block's convolution and deconvolution layer of the decoder block in the V-Net model. Further, nodule classification is performed using a new combination of SqueezeNet and ResNet, named nodule classification network (NCNet). Postprocessing image enhancement is performed on the 2D slice by increasing the image's intensity by adding pseudo-color or fluorescence contrast. The proposed RFR V-Net resulted in dice similarity coefficient of 95.01% and intersection over union of 0.83, respectively. The proposed NCNet achieved the sensitivity of 98.38% and FPs/Scan of 2.3 for 3D representations. The proposed NCNet resulted in considerable improvements over existing CAD systems. © 2021 Wiley Periodicals LLC.
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