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

Muhammad Talha Jahangir; Azhar ud Din; Samina Naz; Naghma Ajmal; Sumaira Parveen; Hamna Rehman

Authors

Muhammad Talha Jahangir Here’s a refined version of the paragraphs, maintaining the reference numbering intact: "This article proposes a design for a low-aperture, high-gain antenna specifically tailored for Wi-Fi applications. This project aims to enhance the performance of Wi-Fi networks by increasing signal strength and coverage area. Conventional dielectric rod antennas face three main challenges: first, they typically have low gain and excessive length; second, they exhibit high side lobe levels; and third, as the antenna length increases, side lobe levels also rise alongside gain. The proposed antenna structure effectively addresses these issues. The novelty of the proposed antenna lies in its ability to achieve greater gain at the same length compared to conventional antennas, while maintaining low side lobe levels that do not increase with antenna length. The proposed design features a Yagi-Uda configuration on a printed circuit board (PCB) made from FR4 epoxy with a dielectric constant of 4.4, combined with a tapering dielectric Teflon rod with a dielectric constant of 2.1. The antenna was simulated using HFSS software, fabricated, and then tested to compare simulated and experimental results, which indicate that the proposed structure primarily operates at a frequency of 5 GHz. It achieves performance within the frequency band of 4.8–5.3 GHz, with a fractional bandwidth of 10%. At these frequencies, the structure provides a directivity of 16.4 dBi. A comparison of results demonstrates that the presented antenna outperforms traditional antennas in the same class, making it suitable for Wi-Fi, WLAN, and satellite applications." This revision enhances clarity, coherence, and overall readability while preserving the original intent and details.
Azhar ud Din Department of Computer Science, MNS University of Engineering and Technology, Multan, Pakistan
Samina Naz Faculty of Computing and Emerging Technologies, Emerson University, Multan, Pakistan
Naghma Ajmal Department of Software Engineering, The Islamia University Bahawalpur, Pakistan
Sumaira Parveen Department of Software Engineering, The Islamia University Bahawalpur, Pakistan
Hamna Rehman Department of Computer Science, MNS University of Engineering and Technology, Multan, Pakistan

Keywords:

Barrett's Esophagus, Cell Vizio, Convolutional Neural Network, Dysplasia, Esophageal Cancer, Gastroesophageal Reflux, Res Net 50, Squamous Epithelium.

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.

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