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 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.