A. Nagaraja Rao
Bio: A. Nagaraja Rao is an academic researcher from VIT University. The author has contributed to research in topics: Computer science & Artificial intelligence. The author has an hindex of 4, co-authored 14 publications receiving 50 citations.
TL;DR: The experimental outcome shows that the proposed methodology improved accuracy in breast cancer classification up to 3% to 9% compared to other existing methods.
Abstract: Breast cancer detection is the most challenging aspect in the field of health monitoring system. In this paper, breast cancer detection was assessed by employing Mammographic Image Analysis Society (MIAS) dataset. The proposed approach contains four major steps, namely, image‐preprocessing, segmentation, feature extraction, and classification. Initially, Laplacian filtering was utilized to identify the area of edges in mammogram images and, also, it was very sensitive to noise. Then, segmentation was carried‐out using modified‐Adaptively Regularized Kernel‐based Fuzzy‐C‐Means (ARKFCM); it was a flexible high level machine learning technique to localize the object in complex template. In conventional ARKFCM, it was hard to segment the ill‐defined masses in mammogram images. To address this concern, the Euclidean distance in ARKFCM was replaced by correlation function in order to improve the segmentation efficiency. The hybrid feature extraction (Histogram of Oriented Gradients (HOG), homogeneity, and energy) was performed on the segmented cancer region to extract feature subsets. The respective feature values were given as the input for a multi‐objective classifier: Deep Neural Network (DNN) for classifying the normal and abnormal regions in mammogram images. The experimental outcome shows that the proposed methodology improved accuracy in breast cancer classification up to 3% to 9% compared to other existing methods.
01 Jan 2007
TL;DR: The objective of the present paper is to obtain an accurate classification of the textures, which did not introduce undesired merging and to develop a quick, effective and novel algorithm that should be easy to understand and implement.
Abstract: Summary: The objective of the present paper is to obtain an accurate classification of the textures, which did not introduce undesired merging and to develop a quick, effective and novel algorithm that should be easy to understand and implement. For this the present study advocates a new statistical method based on edge direction movement for classification of textures on the opening of the image. An edge is a property attached to an individual pixel and is calculated from the image function behavior in a neighborhood of that pixel. Based on this assumption the present study calculated the frequencies of Horizontal, Vertical, Right and Left diagonal patterns on edge direction movement for classification of textures. The experimental results on groups and samples of Brodatz textures show validity of the present method.
••12 Mar 2015
TL;DR: The types of noise models, different types of noises and differentiating of image enhancement techniques are presented to provide a comparative study & exploration of different image enhancement algorithms.
Abstract: Now a day's representation of visual data in digital images is a good way of communication, but the image is degraded with the noise after the transmission. Noise is a major factor that effects image quality which is mainly produced in the methods of image acquirement and transmission. Image processing is required before the image can be utilized. Denoising the images holds the operation of the data of image to yields a visually high quality image. The main focus of this paper is, it presents the types of noise models, different types of noises and differentiating of image enhancement techniques. Here we provide a comparative study & exploration of different image enhancement algorithms.
TL;DR: A novel and simple image segmentation schemes that are based on combinations of morphological and statistical operations and improved, by dynamically changing the combinatorial coefficients that are used in equations are introduced.
Abstract: In this paper we introduce a novel and simple image segmentation schemes that are based on combinations of morphological and statistical operations. Mathematical morphology is very attractive for this purpose because it efficiently deals with geometrical features like as size, shape, contrast or connectivity that can be considered as segmentation oriented features. The present paper derives equations on the basis of dilation, erosion and median or mean which finally results segmentation. The segmentation algorithms are divided into three groups based on number of operations and type of operations, used. Some of the proposed methods of segmentation are useful for edge based segmentation while the other is useful for region based segmentation. The segmentation quality is improved, by dynamically changing the combinatorial coefficients that are used in equations. The present combinatorial method is applied on Brodatz textures and a good segmentation is resulted.
TL;DR: Mamunur et al. as discussed by the authors proposed DeepCervix, a hybrid deep feature fusion (HDFF) technique based on DL, to classify the cervical cells accurately, which achieved the state-of-the-art classification accuracy of 99.85%, 99.38%, and 99.14% for 2-class, 3-class and 5-class classification.
TL;DR: Various communication protocols, namely Zigbee, Bluetooth, Near Field Communication (NFC), LoRA, etc. are presented, and the difference between different communication protocols is provided.
Abstract: Internet of Things (IoT) consists of sensors embed with physical objects that are connected to the Internet and able to establish the communication between them without human intervene applications are industry, transportation, healthcare, robotics, smart agriculture, etc. The communication technology plays a crucial role in IoT to transfer the data from one place to another place through Internet. This paper presents various communication protocols, namely Zigbee, Bluetooth, Near Field Communication (NFC), LoRA, etc. Later, it provides the difference between different communication protocols. Finally, the overall discussion about the communication protocols in IoT.
TL;DR: In this article, a two-route convolutional neural network (CNN) was proposed by extracting global and local features for detecting and classifying COVID-19 infection from CT images, which achieved precision 96%, recall 97%, F score, average surface distance (ASD) of 2.8 ± 0.3 mm, and volume overlap error (VOE) of 5.6 ± 1.2%.
Abstract: The COVID-19 pandemic is a global, national, and local public health concern which has caused a significant outbreak in all countries and regions for both males and females around the world. Automated detection of lung infections and their boundaries from medical images offers a great potential to augment the patient treatment healthcare strategies for tackling COVID-19 and its impacts. Detecting this disease from lung CT scan images is perhaps one of the fastest ways to diagnose patients. However, finding the presence of infected tissues and segment them from CT slices faces numerous challenges, including similar adjacent tissues, vague boundary, and erratic infections. To eliminate these obstacles, we propose a two-route convolutional neural network (CNN) by extracting global and local features for detecting and classifying COVID-19 infection from CT images. Each pixel from the image is classified into the normal and infected tissues. For improving the classification accuracy, we used two different strategies including fuzzy c-means clustering and local directional pattern (LDN) encoding methods to represent the input image differently. This allows us to find more complex pattern from the image. To overcome the overfitting problems due to small samples, an augmentation approach is utilized. The results demonstrated that the proposed framework achieved precision 96%, recall 97%, F score, average surface distance (ASD) of 2.8 ± 0.3 mm, and volume overlap error (VOE) of 5.6 ± 1.2%.
••01 Sep 2012
TL;DR: Experimental results on a variety of material surfaces found in industry, including textured images of plastic surfaces and leather and non-textured image of backside solar wafers and LCD backlight panels, have shown the effectiveness of the proposed regularity measure for surface defect detection.
Abstract: In this paper, we propose a fast regularity measure for defect detection in non-textured and homogeneously textured surfaces, with specific emphasis on ill-defined subtle defects. A small neighborhood window of proper size is first chosen and they slide over the entire inspection image in a pixel-by-pixel basis. The regularity measure for each image patch enclosed in the window is then derived from the eigenvalues of the covariance matrix formed by the variance–covariance of the x- and y-coordinates with the pixel gray levels as the weights for all pixel points in the window. The two eigenvalues of the weighted covariance matrix will be approximately the same when the image patch contains only a homogeneous region, whereas the two eigenvalues will be relatively different if the image patch in the window contains a defect. The smaller eigenvalue of the covariance matrix is then used as the regularity measure. The integral image technique is introduced to the computation of the regularity measure so that it is invariant to the neighborhood window size. The proposed method uses only one single discrimination feature for defect detection. It avoids the use of complicated classifiers in a high-dimensional feature space, and requires no learning process from a set of defective and defect-free training samples. Experimental results on a variety of material surfaces found in industry, including textured images of plastic surfaces and leather and non-textured images of backside solar wafers and LCD backlight panels, have shown the effectiveness of the proposed regularity measure for surface defect detection. It is computationally very fast, and takes only 0.032 s for a 400 × 400 image on a Pentium 3.00 GHz personal computer. In a test set of 73 backside solar wafer images involving 53 defect-free and 20 defective samples, the proposed regularity measure can correctly identify all the test images.
TL;DR: In this article, a computer-vision-based FC-DSCNN CAD system was proposed for the detection of microcalcification clusters from mammograms and classification into malignant and benign classes.
Abstract: Microcalcification clusters in mammograms are one of the major signs of breast cancer. However, the detection of microcalcifications from mammograms is a challenging task for radiologists due to their tiny size and scattered location inside a denser breast composition. Automatic CAD systems need to predict breast cancer at the early stages to support clinical work. The intercluster gap, noise between individual MCs, and individual object’s location can affect the classification performance, which may reduce the true-positive rate. In this study, we propose a computer-vision-based FC-DSCNN CAD system for the detection of microcalcification clusters from mammograms and classification into malignant and benign classes. The computer vision method automatically controls the noise and background color contrast and directly detects the MC object from mammograms, which increases the classification performance of the neural network. The breast cancer classification framework has four steps: image preprocessing and augmentation, RGB to grayscale channel transformation, microcalcification region segmentation, and MC ROI classification using FC-DSCNN to predict malignant and benign cases. The proposed method was evaluated on 3568 DDSM and 2885 PINUM mammogram images with automatic feature extraction, obtaining a score of 0.97 with a 2.35 and 0.99 true-positive ratio with 2.45 false positives per image, respectively. Experimental results demonstrated that the performance of the proposed method remains higher than the traditional and previous approaches.