Article
Computer Science, Artificial Intelligence
Rokaya Rehouma, Michael Buchert, Yi-Ping Phoebe Chen
Summary: COVID-19 is a significant health challenge globally, and early detection is crucial for controlling the spread and reducing mortality rates. Machine learning has made significant progress in COVID-19 detection using medical imaging, with deep learning algorithms widely used for patient identification and achieving good predictive results.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2021)
Review
Oncology
Xiaoyan Jiang, Zuojin Hu, Shuihua Wang, Yudong Zhang
Summary: This article provides a detailed overview of the working mechanisms and use cases of deep learning in medical image-based cancer diagnosis. It discusses the basic architecture of deep learning, pretrained models, methods to overcome overfitting, and the application of deep learning in cancer diagnosis. The article also explores the challenges and future research directions in this field.
Review
Medicine, General & Internal
Fatma A. A. Mostafa, Lamiaa A. A. Elrefaei, Mostafa M. M. Fouda, Aya Hossam
Summary: Thoracic diseases refer to disorders affecting the lungs, heart, and rib cage, such as pneumonia, COVID-19, tuberculosis, etc. Early detection of these diseases is crucial and advances in image processing and deep learning techniques have enabled automated detection. This comprehensive review covers various aspects of deep learning applications in medical thoracic images.
Review
Computer Science, Artificial Intelligence
Mohammad Ahsan, Anam Khan, Kaif Rehman Khan, Bam Bahadur Sinha, Anamika Sharma
Summary: This paper explores the applications of machine learning in the medical field, focusing on radiology, pathology, genomics, and clinical decision-making. It discusses the advantages, drawbacks, and limitations of machine learning, and highlights its potential benefits in diagnosis and personalized treatment programs through a review of previous studies. Additionally, the paper investigates the applications of deep learning and summarizes the main conclusions and their implications for the healthcare sector.
Review
Biology
Shahabedin Nabavi, Azar Ejmalian, Mohsen Ebrahimi Moghaddam, Ahmad Ali Abin, Alejandro F. Frangi, Mohammad Mohammadi, Hamidreza Saligheh Rad
Summary: This study reviewed the characteristics of COVID-19 in medical images, explored the potential of automated artificial intelligence in COVID-19 diagnosis, and proposed the recommendation of collecting more patient imaging data to improve the performance of automated diagnostic methods.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Review
Computer Science, Artificial Intelligence
Mehreen Tariq, Sajid Iqbal, Hareem Ayesha, Ishaq Abbas, Khawaja Tehseen Ahmad, Muhammad Farooq Khan Niazi
Summary: The intervention of medical imaging has greatly improved the early diagnosis of breast cancer, but the meticulous classification of breast abnormalities remains challenging. With the increasing application of Artificial Intelligence in healthcare, researchers are focusing on designing efficient intelligent computer aided detection and diagnosis systems to predict this catastrophic disease.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Qiaoying Teng, Zhe Liu, Yuqing Song, Kai Han, Yang Lu
Summary: This paper comprehensively reviews the interpretability of deep learning in medical diagnosis, including the application of interpretability methods, evaluation metrics, disease datasets, as well as the challenges and future research directions.
MULTIMEDIA SYSTEMS
(2022)
Review
Engineering, Biomedical
Tianming Liu, Eliot Siegel, Dinggang Shen
Summary: The COVID-19 pandemic has presented significant challenges to global healthcare organizations. Thoracic imaging has played a crucial role in diagnosing, predicting, and managing COVID-19 patients with moderate to severe symptoms or worsening respiratory status. The medical image analysis community has responded by quickly developing and sharing deep learning models and tools to handle the large amounts of COVID-19 imaging data. This review summarizes existing methods and provides recommendations for future investigations.
ANNUAL REVIEW OF BIOMEDICAL ENGINEERING
(2022)
Article
Computer Science, Information Systems
Meghavi Rana, Megha Bhushan
Summary: Computer-aided detection using Deep Learning and Machine Learning has shown significant growth in the medical field. Medical images provide essential information for disease diagnosis, and early detection using various modalities is crucial for reducing mortality rates. While Machine Learning has limitations with large amounts of data, Deep Learning works efficiently regardless of data size. This study reviews the applications of Machine Learning and Deep Learning in disease detection and classification, providing an overview of different approaches, evaluation techniques, and datasets. Experiments are conducted using MRI datasets to compare the performance of Machine Learning classifiers and Deep Learning models. This study will assist medical practitioners and researchers in choosing diagnosis techniques with reduced time and high accuracy.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Medicine, General & Internal
Salman Zakareya, Habib Izadkhah, Jaber Karimpour
Summary: Breast cancer is a common and life-threatening disease among women worldwide. Machine learning, specifically deep learning, has shown potential in early detection of breast cancer. This paper proposes a new deep model for breast cancer classification by incorporating techniques such as granular computing, shortcut connection, learnable activation functions, and attention mechanism, and demonstrates its superiority through comparing with existing deep models and case studies.
Article
Oncology
Salem Alkhalaf, Fahad Alturise, Adel Aboud Bahaddad, Bushra M. Elamin Elnaim, Samah Shabana, Sayed Abdel-Khalek, Romany F. Mansour
Summary: Explainable Artificial Intelligence (XAI) technology uses advanced image analysis methods like deep learning to automate cancer diagnosis and provide clear explanations. This study explores the application of XAI and deep-learning models for automated cancer diagnosis, showing the potential of the proposed model for accurate and rapid cancer detection.
Review
Oncology
Qiuxia Wei, Nengren Tan, Shiyu Xiong, Wanrong Luo, Haiying Xia, Baoming Luo
Summary: This study comprehensively reviewed 1356 papers on the diagnostic performance of deep learning methods in hepatocellular carcinoma (HCC) based on medical images. The findings showed that deep learning methods demonstrated high sensitivity, specificity, and accuracy in HCC diagnosis, similar to human clinicians.
Article
Computer Science, Information Systems
Zhiwei Guo, Yu Shen, Shaohua Wan, Wen-Long Shang, Keping Yu
Summary: In this paper, a hybrid intelligence-driven medical image recognition framework combining deep learning with conventional machine learning is proposed to solve the issue of remote patient diagnosis in smart cities. Experimental results reveal that the framework improves recognition accuracy by approximately two to three percent compared to traditional methods.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2022)
Article
Biochemical Research Methods
Yunwei Fan, Li Li, Ping Chu, Qian Wu, Yuan Wang, WenHong Cao, Ningdong Li
Summary: This study proposed an optimization scheme involving a video eye tracker combined with an artificial eye to comprehensively assess eye movement in children with amblyopia. The results showed that the eye movement parameters of amblyopic eyes and eyes with normal vision are significantly different, and certain parameters can be used to supplement and optimize the diagnostic criteria for amblyopia, providing a diagnostic basis for evaluating binocular visual function.
Review
Computer Science, Information Systems
Manisha Singh, Gurubasavaraj Veeranna Pujar, Sethu Arun Kumar, Meduri Bhagyalalitha, Handattu Shankaranarayana Akshatha, Belal Abuhaija, Anas Ratib Alsoud, Laith Abualigah, Narasimha M. Beeraka, Amir H. Gandomi
Summary: Tuberculosis is a major infectious disease that poses a threat to global human health, with timely diagnosis and treatment being crucial. Computer-aided diagnosis using machine learning has become a promising choice, with deep learning offering a larger scope for diagnosing tuberculosis effectively.