Thyroid Cancer Computer-Aided Diagnosis System using MRI-Based Multi-Input CNN Model

Naglah, Ahmed M.; Khalifa, Fahmi; Khaled, Reem; Razek, Ahmed Abdel Khalek Abdel; El‐Baz, Ayman

doi:10.1109/isbi48211.2021.9433841

Cited by 14 publications

(3 citation statements)

References 17 publications

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“…At the moment, it remains controversial whether the diagnostic performance of conventional CAD systems can be comparable to experienced sonographers (15). There are many studies and applications for using neural networks for CAD (16). Four studies showed that US CAD systems improved the diagnostic accuracy of thyroid US and could help junior physicians make a diagnosis (17)(18)(19)(20).…”

Section: Resultsmentioning

confidence: 99%

Diagnostic value of a computer-assisted diagnosis system for the ultrasound features in thyroid nodules

Wang,

Yu,

et al. 2023

Archives of Endocrinology and Metabolism

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Objective: To explore the diagnostic value of the TUIAS (SW_TH01/II) computer-aided diagnosis (CAD) software system for the ultrasound Thyroid Imaging Reporting and Data System (TI-RADS) features in thyroid nodules. Materials and methods: This retrospective study enrolled patients with thyroid nodules in Shanghai East Hospital between January 2017 and October 2021. The novel CAD software (SW_TH01/II) and three sonographers performed a qualitative analysis of the ultrasound TI-RADS features in aspect ratio, margin irregularity, margin smoothness, calcification, and echogenicity of the thyroid nodules. Results: A total of 225 patients were enrolled. The accuracy, sensitivity, and specificity of the CAD software in “aspect ratio” were 95.6%, 96.2%, and 95.4%, in “margin irregularity” were 90.7%, 90.5%, and 90.9%, in “margin smoothness” were 85.8%, 88.5%, and 83.0%, in “calcification” were 83.6%, 81.7%, and 82.0%, in “homogeneity” were 88.9%, 90.6%, and 82.2%, in “major echo” were 85.3%, 88.0%, and 85.4%, and in “contains very hypoechoic echo” were 92.0%, 90.0%, and 92.4%. The analysis time of the CAD software was significantly shorter than for the sonographers (2.7 ± 1.6 vs. 29.7 ± 12.7 s, P < 0.001). Conclusion: The CAD system achieved high accuracy in describing thyroid nodule features. It might assist in clinical thyroid nodule analysis.

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Section: Resultsmentioning

confidence: 99%

Diagnostic value of a computer-assisted diagnosis system for the ultrasound features in thyroid nodules

Wang,

Yu,

et al. 2023

Archives of Endocrinology and Metabolism

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“…Inspired by the base model of CNN [ 35 , 36 ], proving the viability of the multi-input CNN model [ 37 , 38 , 39 ], DICNN-XAI is proposed in the study. To increase robustness, DICNN updates a number of parameters adaptively from numerous inputs [ 40 ] and aids in the identification of deep texture patterns [ 41 ]. Two input layers (size 224 × 224 ×3) were defined.…”

Section: Methodsmentioning

confidence: 99%

Auguring Fake Face Images Using Dual Input Convolution Neural Network

et al. 2022

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Deepfake technology uses auto-encoders and generative adversarial networks to replace or artificially construct fine-tuned faces, emotions, and sounds. Although there have been significant advancements in the identification of particular fake images, a reliable counterfeit face detector is still lacking, making it difficult to identify fake photos in situations with further compression, blurring, scaling, etc. Deep learning models resolve the research gap to correctly recognize phony images, whose objectionable content might encourage fraudulent activity and cause major problems. To reduce the gap and enlarge the fields of view of the network, we propose a dual input convolutional neural network (DICNN) model with ten-fold cross validation with an average training accuracy of 99.36 ±0.62, a test accuracy of 99.08 ± 0.64, and a validation accuracy of 99.30 ± 0.94. Additionally, we used ’SHapley Additive exPlanations (SHAP) ’ as explainable AI (XAI) Shapely values to explain the results and interoperability visually by imposing the model into SHAP. The proposed model holds significant importance for being accepted by forensics and security experts because of its distinctive features and considerably higher accuracy than state-of-the-art methods.

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“…In [ 21 ], the author explains that malignant nodules can be detected by using an MRI-based “computer assisted diagnostic” (CAD) system. A multi-input convolutional neural network is used in our method to merge the diffusion weighted image (DWI) and “apparent diffusion coefficient” (ADC) maps from MRI scans.…”

Section: Related Workmentioning

confidence: 99%

Analysis of the Application Value of Ultrasound Imaging Diagnosis in the Clinical Staging of Thyroid Cancer

Zhang

Sun

et al. 2022

Journal of Oncology

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Thyroid cancer affects 1.3 percent of the population, with rates of occurrence rising in recent years (approximately 2 percent per year). Thyroid cancer is a common endocrine cancer with an annual increase in occurrence. Although the general prognosis for differentiated subtypes is favorable, the rate of mortality linked with thyroid cancer has been steadily progressing. The presence of suspicious thyroid nodules necessitates more diagnostic testing, including laboratory evaluation, additional imaging, and biopsy. For clinical staging and appropriate patient therapy design, accurate diagnosis is necessary. In this paper, we examined the application value of ultrasound imaging diagnosis in the clinical staging of thyroid tumor in this research. The benefit of early diagnosis is determined in this article using ultrasonography reports from Chinese patients. Images of benign and malignant thyroid nodules were collected and annotated in this work, and deep learning-based image recognition and diagnostic system was built utilizing the adaptive wavelet transform-based AdaBoost algorithm (AWT-AA). The system’s efficacy in diagnosing thyroid nodules was assessed, and the use of ultrasound imaging in clinical practice was studied. The variables that had a significant impact on malignant nodules were studied using logistic multiple regression analysis. The sensitivity and specificity of ultrasonography thyroid imaging reporting and data system (TI-RADS) categorization outcomes for benign and malignant tumors were also calculated.

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Thyroid Cancer Computer-Aided Diagnosis System using MRI-Based Multi-Input CNN Model

Cited by 14 publications

References 17 publications

Diagnostic value of a computer-assisted diagnosis system for the ultrasound features in thyroid nodules

Diagnostic value of a computer-assisted diagnosis system for the ultrasound features in thyroid nodules

Auguring Fake Face Images Using Dual Input Convolution Neural Network

Analysis of the Application Value of Ultrasound Imaging Diagnosis in the Clinical Staging of Thyroid Cancer

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