Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Classification of Capsule Endoscopy Images based on Feature Concatenation of Deep Neural Networks(Institute of Electrical and Electronics Engineers Inc., 2021) Biradher, S.; Aparna., P.Wireless capsule endoscopy (WCE) is a noninvasive way of detecting abnormalities in digestive tract. These abnormalities need to be detected at the early stages before they turn malignant. The classification of these abnormalities has put many challenges due to the variations in lesion shape and color, lighting conditions, and other factors. Existing methods based on handcrafted features give less accuracy due to the limited capability of feature representation. This study proposes a new approach for classifying wireless capsule endoscopy images using feature concatenation of deep convolutional neural network models. The features of two pre-trained models are concatenated and tested using a newly created dataset. The dataset is created using images taken from the Kvasir capsule endoscopy and Red lesion endoscopy dataset which is publicly available. This system improves diagnostic efficiency and brings great assistance to the doctor. © 2021 IEEE.Item Blindness (Diabetic Retinopathy) Severity Scale Detection(Institute of Electrical and Electronics Engineers Inc., 2021) Bygari, R.; Naik, R.; Uday Kumar, P.Diabetic retinopathy (DR) is a severe complication of diabetes that can cause permanent blindness. Timely diagnosis and treatment of DR are critical to avoid total loss of vision. Manual diagnosis is time consuming and error-prone. In this paper, we propose a novel deep learning based method for automatic screening of retinal fundus images to detect and classify DR based on the severity. The method uses a dual-path configuration of deep neural networks to achieve the objective. In the first step, a modified UNet++ based retinal vessel segmentation is used to create a fundus image that emphasises elements like haemorrhages, cotton wool spots, and exudates that are vital to identify the DR stages. Subsequently, two convolutional neural networks (CNN) classifiers take the original image and the newly created fundus image respectively as inputs and identify the severity of DR on a scale of 0 to 4. These two scores are then passed through a shallow neural network classifier (ANN) to predict the final DR stage. The public datasets STARE, DRIVE, CHASE DB1, and APTOS are used for training and evaluation. Our method achieves an accuracy of 94.80% and Quadratic Weighted Kappa (QWK) score of 0.9254, and outperform many state-of-the-art methods. © 2021 IEEE.Item Classification of Wireless Capsule Endoscopy Bleeding Images using Deep Neural Network(Institute of Electrical and Electronics Engineers Inc., 2022) Biradher, S.; Aparna., P.Wireless capsule endoscopy (WCE) is an innovative video technique that allows a non-invasive visual inspection of the whole gastrointestinal system. The digestive tract anomalies must be identified in advance to treat them before they turn into hazardous cancers. Detection and classification of digestive tract associated anomalies have been difficult because of various factors like differences in lesion form and color, level of illumination or lighting, etc. Existing approaches based on non-deep learning methods include a manually designed feature extraction step, which is less efficient since these manually designed features may lose essential information and cannot be optimised because they are not part of an end-to-end learning system. This paper provides a new way of classification between bleeding and non-bleeding classes of wireless capsule endoscopy images. The proposed method makes use of a simple Deep Convolutional Neural Network which consists of six convolutional layers alternated with max-pooling layers and this technique is compared with existing ones in terms of different performance metrics. This approach boosts diagnostic efficiency and gives doctors a huge amount of support. © 2022 IEEE.Item Transaction fee forecasting in post EIP-1559 Ethereum using 1-D Convolutional Neural Network(Institute of Electrical and Electronics Engineers Inc., 2023) Kallurkar, H.S.; Chandavarkar, B.R.Cryptocurrencies have established their identity as a healthy alternative to the maintenance of digital assets. Their applications include low-cost money transfers and yield farming. Ethereum is a blockchain that provides the functionality of doing more than a transaction regarding cryptocurrency. Ether is the default cryptocurrency of Ethereum, which is issued to the miners after the successful completion of the consensus mechanism to avoid fraudulent miners gaining profits. Transactions in Ether require that the user should include what is called a 'fee' besides the amount that is sent by the user. The EIP-1559 (Ethereum Improvement Proposals) upgrade to the Ethereum protocol has substantially changed how the transaction fee is calculated. Since this transaction data can be considered time-series data, many prior approaches have been proposed to forecast such a transaction fee using suitable methods effectively. One-dimensional Convolutional Neural Networks have recently been successfully applied to time-series forecasting problems, showing promising results. This paper proposes a univariate 1-D CNN for an effective forecast of transaction fees in the new Ethereum protocol. Furthermore, this paper also compares the proposed method with existing standard approaches, and the results show the superior performance of simple 1-dimensional convolutional neural networks over existing hybrid models. © 2023 IEEE.