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      Enhancing Disease Classification in Abdominal CT Scans through RGB Superposition Methods and 2D Convolutional Neural Networks: A Study of Appendicitis and Diverticulitis


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          The primary symptom of both appendicitis and diverticulitis is a pain in the right lower abdomen; it is almost impossible to diagnose these conditions through symptoms alone. However, there will be misdiagnoses happening when using abdominal computed tomography (CT) scans. Most previous studies have used a 3D convolutional neural network (CNN) suitable for processing sequences of images. However, 3D CNN models can be difficult to implement in typical computing systems because they require large amounts of data, GPU memory, and extensive training times. We propose a deep learning method, utilizing red, green, and blue (RGB) channel superposition images reconstructed from three slices of sequence images. Using the RGB superposition image as the input image of the model, the average accuracy was shown as 90.98% in EfficietNetB0, 91.27% in EfficietNetB2, and 91.98% in EfficietNetB4. The AUC score using the RGB superposition image was higher than the original image of the single channel for EfficientNetB4 (0.967 vs. 0.959, p = 0.0087). The comparison in performance between the model architectures using the RGB superposition method showed the highest learning performance in the EfficientNetB4 model among all indicators; accuracy was 91.98% and recall was 95.35%. EfficientNetB4 using the RGB superposition method had a 0.011 ( p value = 0.0001) AUC score higher than EfficientNetB0 using the same method. The superposition of sequential slice images in CT scans was used to enhance the distinction in features like shape, size of the target, and spatial information used to classify disease. The proposed method has fewer constraints than the 3D CNN method and is suitable for an environment using 2D CNN; thus, we can achieve performance improvement with limited resources.

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          Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization

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            Appendicitis at the millennium.

            Acute appendicitis is a common clinical problem. Accurate and prompt diagnosis is essential to minimize morbidity. While the clinical diagnosis may be straightforward in patients who present with classic signs and symptoms, atypical presentations may result in diagnostic confusion and delay in treatment. Helical computed tomography (CT) and graded compression color Doppler ultrasonography (US) are highly accurate means of establishing the diagnosis. These imaging modalities have now assumed critical roles in the treatment of patients suspected to have appendicitis. The purpose of this article is threefold: to provide an update on new information regarding the pathophysiology, clinical diagnosis, and laparoscopic treatment of acute appendicitis; to describe the state-of-the art use of CT and US in diagnosing this disease entity; and to address the role of medical imaging in this patient population.
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              A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises


                Author and article information

                Comput Math Methods Med
                Comput Math Methods Med
                Computational and Mathematical Methods in Medicine
                29 May 2023
                : 2023
                : 7714483
                1Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University, Incheon, Republic of Korea
                2Department of Radiology, Gil Medical Center, Gachon University College of Medicine, Incheon, Republic of Korea
                3Department of Biomedical Engineering, College of IT Convergence, Gachon University, Gyeonggi-do, Republic of Korea
                4Division of Gastroenterology, Department of Internal Medicine, Gil Medical Center, Gachon University College of Medicine, Incheon, Republic of Korea
                5Department of Biomedical Engineering Medical Center, College of Medicine, Gachon University, Incheon, Republic of Korea
                Author notes

                Academic Editor: Nagarajan DeivanayagamPillai

                Author information
                Copyright © 2023 Gi Pyo Lee et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                : 2 November 2022
                : 10 February 2023
                : 15 April 2023
                Funded by: Gachon Program
                Award ID: GCU-202209140001
                Funded by: GRRC Program of Gyeonggi Province
                Award ID: GRRC-Gachon2020(B02)
                Research Article

                Applied mathematics
                Applied mathematics


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