R. S. Anand

Bio: R. S. Anand is an academic researcher from Indian Institute of Technology Roorkee. The author has contributed to research in topics: Thresholding & Wavelet. The author has an hindex of 28, co-authored 133 publications receiving 2323 citations. Previous affiliations of R. S. Anand include Indian Institutes of Technology & Indian Institute of Technology Kanpur.

Epileptic seizures detection in EEG using DWT-based ApEn and artificial neural network

Abstract: There are numerous neurological disorders such as dementia, headache, traumatic brain injuries, stroke, and epilepsy. Out of these epilepsy is the most prevalent neurological disorder in the human after stroke. Electroencephalogram (EEG) contains valuable information related to different physiological state of the brain. A scheme is presented for detecting epileptic seizures from EEG data recorded from normal subjects and epileptic patients. The scheme is based on discrete wavelet transform (DWT) analysis and approximate entropy (ApEn) of EEG signals. Seizure detection is performed in two stages. In the first stage, EEG signals are decomposed by DWT to calculate approximation and detail coefficients. In the second stage, ApEn values of the approximation and detail coefficients are calculated. Significant differences have been found between the ApEn values of the epileptic and the normal EEG allowing us to detect seizures with 100 % classification accuracy using artificial neural network. The analysis results depicted that during seizure activity, EEG had lower ApEn values compared to normal EEG. This gives that epileptic EEG is more predictable or less complex than the normal EEG. In this study, feed-forward back-propagation neural network has been used for classification and training algorithm for this network that updates the weight and bias values according to Levenberg–Marquardt optimization technique.

A Transfer Learning approach for AI-based classification of brain tumors

Abstract: Classification of Brain Tumor (BT) is a vital assignment for assessing Tumors and making a suitable treatment. There exist numerous imaging modalities that are utilized to identify tumors in the brain. Magnetic Resonance Imaging (MRI) is generally utilized for such a task because of its unrivaled quality of the image and the reality that it does not depend on ionizing radiations. The relevance of Artificial Intelligence (AI) in the form of Deep Learning (DL) in the area of medical imaging has paved the path to extraordinary developments in categorizing and detecting intricate pathological conditions, like a brain tumor, etc. Deep learning has demonstrated an astounding presentation, particularly in segmenting and classifying brain tumors. In this work, the AI-based classification of BT using Deep Learning Algorithms are proposed for the classifying types of brain tumors utilizing openly accessible datasets. These datasets classify BTs into (malignant and benign). The datasets comprise 696 images on T1-weighted images for testing purposes. The projected arrangement accomplishes a noteworthy performance with the finest accuracy of 99.04%. The achieved outcome signifies the capacity of the proposed algorithm for the classification of brain tumors.

Flaw detection in radiographic weld images using morphological approach

Abstract: It is necessary to detect suspected defect regions in the radiographic weld images to find the type of flaw and its causative factors. This requires processing of radiographic images by a suitable approach. This paper presents an approach to process these radiographic weld images of the weld specimens considering morphological aspects of the image. The proposed approach first determines the flaw boundaries by applying the Canny operator after choosing an appropriate threshold value. The boundaries are then fixed using a morphological image processing approach i.e. dilating few similar boundaries and eroding some irrelevant boundaries decided on the basis of pixel characteristics. The flaws detected by this approach are categorized according to their properties.

Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks

Abstract: A number of image processing techniques IPTs have been implemented for detecting civil infrastructure defects to partially replace human-conducted onsite inspections. These IPTs are primarily used to manipulate images to extract defect features, such as cracks in concrete and steel surfaces. However, the extensively varying real-world situations e.g., lighting and shadow changes can lead to challenges to the wide adoption of IPTs. To overcome these challenges, this article proposes a vision-based method using a deep architecture of convolutional neural networks CNNs for detecting concrete cracks without calculating the defect features. As CNNs are capable of learning image features automatically, the proposed method works without the conjugation of IPTs for extracting features. The designed CNN is trained on 40 K images of 256 × 256 pixel resolutions and, consequently, records with about 98% accuracy. The trained CNN is combined with a sliding window technique to scan any image size larger than 256 × 256 pixel resolutions. The robustness and adaptability of the proposed approach are tested on 55 images of 5,888 × 3,584 pixel resolutions taken from a different structure which is not used for training and validation processes under various conditions e.g., strong light spot, shadows, and very thin cracks. Comparative studies are conducted to examine the performance of the proposed CNN using traditional Canny and Sobel edge detection methods. The results show that the proposed method shows quite better performances and can indeed find concrete cracks in realistic situations.