Abstract
Endoscopy is being the most extensively used diagnostic tool for treatment of organs inside humans. However, many challenges exist that are faced by doctors (endoscopists) including: visual interpretation difficulty because of objects, problems in the identification of cancer abnormalities, and pre-cancerous precursors. In this research paper, an analysis has been performed for classification of diseases such as: non-dysplastic Barrett’s esophagus (BE), subtle pre-cancerous lesion (suspicious), suspected dysplasia (HGD), adenocarcinoma (cancer), and polyp approach using ML and DL. Firstly, machine learning using AI is implemented on the dataset to train the model, but the model is not trained properly because the lesser number of images are used for training the model. Therefore, to ensure higher accuracy, image augmentation is applied on the given dataset of images so that number of images used to train the model can be increased. Here, a tremendous improvement can be vividly seen, but to further improve the accuracy, the augmentation and DL are amalgamated together and the comparison is then made with the existing ML-based augmentation approach. The outcome of DL with augmentation seems to dominate ML-based augmentation. As the epoch’s value keeps on incrementing, the accuracy percentage also increases. Various transfer learning methods show higher accuracy in DL-based augmentation approach.
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Angurala, M., Khullar, V., Singh, P. (2023). Experimenting Transfer Learning and Machine Learning on Endoscopy Database with Augmentation. In: Rani, A., Kumar, B., Shrivastava, V., Bansal, R.C. (eds) Signals, Machines and Automation. SIGMA 2022. Lecture Notes in Electrical Engineering, vol 1023. Springer, Singapore. https://doi.org/10.1007/978-981-99-0969-8_68
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DOI: https://doi.org/10.1007/978-981-99-0969-8_68
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