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4. Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review

Author

Jafari, Mahboobeh, Shoeibi, Afshin, Khodatars, Marjane, Ghassemi, Navid, Moridian, Parisa, Delfan, Niloufar, Alizadehsani, Roohallah, Khosravi, Abbas, Ling, Sai Ho, Zhang, Yu-Dong, Wang, Shui-Hua, Gorriz, Juan M., Rokny, Hamid Alinejad, and Acharya, U. Rajendra

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Machine Learning
Abstract

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia, cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina are the most common CVDs. Clinical methods such as blood tests, electrocardiography (ECG) signals, and medical imaging are the most effective methods used for the detection of CVDs. Among the diagnostic methods, cardiac magnetic resonance imaging (CMR) is increasingly used to diagnose, monitor the disease, plan treatment and predict CVDs. Coupled with all the advantages of CMR data, CVDs diagnosis is challenging for physicians due to many slices of data, low contrast, etc. To address these issues, deep learning (DL) techniques have been employed to the diagnosis of CVDs using CMR data, and much research is currently being conducted in this field. This review provides an overview of the studies performed in CVDs detection using CMR images and DL techniques. The introduction section examined CVDs types, diagnostic methods, and the most important medical imaging techniques. In the following, investigations to detect CVDs using CMR images and the most significant DL methods are presented. Another section discussed the challenges in diagnosing CVDs from CMR data. Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined. The most important findings of this study are presented in the conclusion section.

Published
2022

5. Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence

Author

Jafari, Mahboobeh, Shoeibi, Afshin, Ghassemi, Navid, Heras, Jonathan, Ling, Sai Ho, Beheshti, Amin, Zhang, Yu-Dong, Wang, Shui-Hua, Alizadehsani, Roohallah, Gorriz, Juan M., Acharya, U. Rajendra, and Rokny, Hamid Alinejad

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Myocarditis is a significant cardiovascular disease (CVD) that poses a threat to the health of many individuals by causing damage to the myocardium. The occurrence of microbes and viruses, including the likes of HIV, plays a crucial role in the development of myocarditis disease (MCD). The images produced during cardiac magnetic resonance imaging (CMRI) scans are low contrast, which can make it challenging to diagnose cardiovascular diseases. In other hand, checking numerous CMRI slices for each CVD patient can be a challenging task for medical doctors. To overcome the existing challenges, researchers have suggested the use of artificial intelligence (AI)-based computer-aided diagnosis systems (CADS). The presented paper outlines a CADS for the detection of MCD from CMR images, utilizing deep learning (DL) methods. The proposed CADS consists of several steps, including dataset, preprocessing, feature extraction, classification, and post-processing. First, the Z-Alizadeh dataset was selected for the experiments. Subsequently, the CMR images underwent various preprocessing steps, including denoising, resizing, as well as data augmentation (DA) via CutMix and MixUp techniques. In the following, the most current deep pre-trained and transformer models are used for feature extraction and classification on the CMR images. The findings of our study reveal that transformer models exhibit superior performance in detecting MCD as opposed to pre-trained architectures. In terms of DL architectures, the Turbulence Neural Transformer (TNT) model exhibited impressive accuracy, reaching 99.73% utilizing a 10-fold cross-validation approach. Additionally, to pinpoint areas of suspicion for MCD in CMRI images, the Explainable-based Grad Cam method was employed.

Published
2022

25. STResNet: Covid-19 Detection by ResNet Transfer Learning and Stochastic Pooling

Author

Wang, Wei, Wang, Shui-Hua, Zhang, Yu-Dong, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Su, Ruidan, editor, Zhang, Yudong, editor, Liu, Han, editor, and F Frangi, Alejandro, editor

Published
2023
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43. RDNet: ResNet-18 with Dropout for Blood Cell Classification

Author

Zhu, Ziquan, Ren, Zeyu, Wang, Shui-Hua, Górriz, Juan M., Zhang, Yu-Dong, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Ferrández Vicente, José Manuel, editor, Álvarez-Sánchez, José Ramón, editor, de la Paz López, Félix, editor, and Adeli, Hojjat, editor

Published
2022
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44. ConvNet-CA: A Lightweight Attention-Based CNN for Brain Disease Detection

Author

Zhu, Hengde, Wang, Jian, Wang, Shui-Hua, Zhang, Yu-Dong, Górriz, Juan M., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Ferrández Vicente, José Manuel, editor, Álvarez-Sánchez, José Ramón, editor, de la Paz López, Félix, editor, and Adeli, Hojjat, editor

Published
2022
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45. Covid-19 Detection by Wavelet Entropy and Self-adaptive PSO

Author

Wang, Wei, Wang, Shui-Hua, Górriz, Juan Manuel, Zhang, Yu-Dong, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Ferrández Vicente, José Manuel, editor, Álvarez-Sánchez, José Ramón, editor, de la Paz López, Félix, editor, and Adeli, Hojjat, editor

Published
2022
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48. New research progress of microwave ablation in the local precise treatment of early-stage breast cancer: the prospect of combined immunotherapy is promising

Author

ZHOU Wenbin, XIE Hui, DING Qiang, WANG Shui

Subjects
breast cancer, local therapy, microwave ablation, immunotherapy, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Breast cancer is the most common malignant tumor in women. Surgery is the main treatment for early breast cancer. At present, great progress has been made in the diagnosis and treatment of early breast cancer. Howover, innovative approaches are still needed to reduce trauma and improve patients' outcomes. With the progress of innovative research on the treatment of breast cancer, non-surgical treatment of breast and axilla has become a research hotspot of treatment of early breast cancer. Microwave ablation, as a minimally invasive and precise local treatment, has the characteristics of speedy ablation, simple operation method, less postoperative complications and so on. At present, microwave ablation has been used to treat early breast cancer, and has achieved clinical efficacy. In addition, some studies have been reported that microwave ablation of early breast cancer is conducive to regulating the tumor immune microenvironment and stimulating the antigen-specific anti-tumor immune effect in the body. This immune effect may be related to the massive release of tumor antigen after microwave ablation. The effect of immunotherapy alone for breast cancer is poor. Some researchers have proposed a scheme to enhance the ablation immune effect through combined immunotherapy, but its specific effect still needs more clinical data support. With more and more research on the immune effect induced by ablation therapy, microwave ablation combined with immune checkpoint inhibitor is expected to become a new strategy in the precise treatment of breast tumor. This article summarized the new progress and future development direction of microwave ablation in local precise treatment of breast cancer.

Published
2022
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