Exploring the Efficacy of CNN Architectures for Esophageal Cancer Classification Using Cell Vizio Images
Journal Title: International Journal of Innovations in Science and Technology - Year 2024, Vol 6, Issue 4
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 of the existing diagnostic technologies. To address this problem, a new Convolutional Neural Network and ResNet50 architecture are presented 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 improving the 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 be used as a quick, non-embolic, accurate method for early detection of esophageal carcinoma.
Authors and Affiliations
Muhammad Talha Jahangir, Azhar ud Din, Samina Naz, Naghma Ajmal, Sumaira Parveen, Hamna Rehman
Keywords
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