Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
5 results
Search Results
Item Automated Summarization of Gastrointestinal Endoscopy Video(Springer Science and Business Media Deutschland GmbH, 2023) Sushma, B.; Aparna., P.Gastrointestinal (GI) endoscopy enables many minimally invasive procedures for diagnosing diseases such as esophagitis, ulcer, polyps and cancers. Guided by the endoscope’s video sequence, a physician can diagnose the diseases and administer the treatment. Unfortunately, due to the huge amount of data generated, physicians are currently discarding procedural video and rely on a small number of carefully chosen images to record a procedure. In addition, when a patient seeks a second opinion, the assessment of lesions in a huge video stream necessitates a thorough examination, which is a time-consuming process that demands much attention. To reduce the length of the video stream, an automated method to generate the summary of endoscopy video recordings consisting only of abnormal frames by using deep convolutional neural networks trained to classify normal, abnormal and uninformative frames is proposed. Results show that our method can efficiently detect abnormal frames and is robust to the variations in the frames. The proposed CNN architecture outperforms the other classification models with an accuracy of 0.9698 with less number of parameters. © IFIP International Federation for Information Processing 2023.Item Multi-class Classification of Wireless Capsule Endoscopy Images By Using the Fusion of Pre-trained Networks and Fusion Residual Block(Springer Science and Business Media Deutschland GmbH, 2025) Lavanya, K.; Aparna., P.In order to identify diverse endoscopic images of gastrointestinal (GI) problems, this study presented a multi-fused residual convolutional neural network (MFuRe-CNN) with auxiliary fusion layers (AuxFL) and a fusion residual block (FuRB) with alpha dropouts (ADO). The implemented MFuRe-CNN dealt with five instances sourced from reputed databases such as the KVASIR database, the ETIS-Larib Polyp Database, and the Red Lesion Endoscopic Database, which included Esophagitis, Ulcerative colitis, Polyps, Healthy colon and Bleeding images. The proposed model was created by fusing three cutting-edge models fused into a single-feature extraction pipeline with its layers that are truncated and partially frozen. This model which despite using a small portion of the computing power of most current cutting-edge models, helped spread robust features and enhanced diagnostic performance. In addition, compared to those without the aforementioned components, the MFuRe-CNN in conjunction with AuxFLs, DOs and FuRB has significantly reduced overfitting and performance saturation. With merely 4.8 million parameters, this model achieved test accuracy of 93.79 percent, outperforming the majority of DCNNs that are trained in the traditional manner. Thus, the suggested MFuRe-CNN could improve GI tract diagnosis more cheaply than ensembles and outperform other traditional pre-trained as well as fine-tuned Deep convolutional neural networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.Item Distributed video coding based on classification of frequency bands with block texture conditioned key frame encoder for wireless capsule endoscopy(Elsevier Ltd, 2020) Sushma, B.; Aparna., P.Wireless capsule endoscopy (WCE) has provided remarkable improvement in diagnosing gastrointestinal disorders by scanning the entire digestive tract. The system still need refinement, to upgrade the quality of images, frame rate and battery life. The principal component of the system that can address these issues is low complexity video compressor. Motivated by low computational complexity requirements of WCE video encoding, this paper presents a distributed video coding framework based on frequency bands classification. The lower frequency bands are used to generate good quality side information (SI) as they exhibit high temporal correlation. This reduces the complexity of hash generation at the encoder, thus eliminating the latency in SI creation. Apart from this, SI creation involves only a simple block search and doesn't depend on Wyner–Ziv (WZ) bands. Also different approach for distributed coding of sub-sampled chroma components of WZ frame is proposed. Low complexity JPEG based key frame encoding is proposed that take advantage of WCE image textural properties to reduce the complexity of encoding smooth blocks by 81% at the quantization and encoding stage. Other novel features include use of discrete Tchebichef transform (DTT), Golomb–Rice code for entropy coding. Performance evaluation shows that the proposed method achieves 60% improvement in compression over Motion JPEG with low computational complexity. © 2020 Elsevier LtdItem Summarization of Wireless Capsule Endoscopy Video Using Deep Feature Matching and Motion Analysis(Institute of Electrical and Electronics Engineers Inc., 2021) Sushma, B.; Aparna., P.Conventional Wireless capsule endoscopy (WCE) video summary generation techniques apprehend an image by extracting hand crafted features, which are not essentially sufficient to encapsulate the semantic similarity of endoscopic images. Use of supervised methods for extraction of deep features from an image need an enormous amount of accurate labelled data for training process. To solve this, we use an unsupervised learning method to extract features using convolutional auto encoder. Furthermore, WCE images are classified into similar and dissimilar pairs using fixed threshold derived through large number of experiments. Finally, keyframe extraction method based on motion analysis is used to derive a structured summary of WCE video. Proposed method achieves an average F-measure of 91.1% with compression ratio of 83.12%. The results indicate that the proposed method is more efficient compared to existing WCE video summarization techniques. © 2013 IEEE.Item Deep chroma prediction of Wyner–Ziv frames in distributed video coding of wireless capsule endoscopy video(Academic Press Inc., 2022) Sushma, B.; Aparna., P.Compression of captured video frames is crucial for saving the power in wireless capsule endoscopy (WCE). A low complexity encoder is desired to limit the power consumption required for compressing the WCE video. Distributed video coding (DVC) technique is best suitable for designing a low complexity encoder. In this technique, frames captured in RGB colour space are converted into YCbCr colour space. Both Y and CbCr representing luma and chroma components of the Wyner–Ziv (WZ) frames are processed and encoded in existing DVC techniques proposed for WCE video compression. In the WCE video, consecutive frames exhibit more similarity in texture and colour properties. The proposed work uses these properties to present a method for processing and encoding only the luma component of a WZ frame. The chroma components of the WZ frame are predicted by an encoder–decoder based deep chroma prediction model at the decoder by matching luma and texture information of the keyframe and WZ frame. The proposed method reduces the computations required for encoding and transmitting of WZ chroma component. The results show that the proposed DVC with a deep chroma prediction model performs better when compared to motion JPEG and existing DVC systems for WCE at the reduced encoder complexity. © 2022 Elsevier Inc.