Exploring the Efficacy of CNN Architectures for Esophageal Cancer Classification Using Cell Vizio Images

Muhammad Talha Jahangir ()
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Muhammad Talha Jahangir: Department of Computer Science, MNS University of Engineering and Technology, Multan, Pakistan

International Journal of Innovations in Science & Technology, 2024, vol. 6, issue 4, 1720-1735

Abstract: Esophageal cancer, as with the global burden of disease, is usually due to Barrret's esophagus and gastroesophageal reflux disease. Fortunately, the disease is amenable to early detection; however, early diagnosis has been complicated by the limitations ofthe existing diagnostic technologies. To address this problem, a new Convolutional Neural Network and ResNet50 architecture arepresented in this study to aid esophageal cancer diagnosis through the classification of Cell Vizio images. This diagnosis is made by the deep learning architecture which does tissue classification into four categories thus improvingthe diagnostic sensitivity. For model training and testing preoperative perforations in sixty-one patients, 11,161 images were used. Data augmentation and normalization techniques were also performed on the images to help improve the outcome. Our training accuracy reached an impressive 99% 12, while our final f1 score was 93.05%. Our Res Net 50 model obtained an F1 score of 93.26%, precision of 94.05%, recall of 93.52 %, and validation accuracy of 93.32 %. These results indicate how well our deep learning-based technique can beused as a quick, non-embolic, accurate method for early detection of esophageal carcinoma.

Keywords: Barrett's Esophagus; Cell Vizio; Convolutional Neural Network; Dysplasia; Esophageal Cancer; Gastroesophageal Reflux; Res Net 50; Squamous Epithelium (search for similar items in EconPapers)
Date: 2024
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