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Abdul Rahaman Wahab Sait and Ali Mohammad Alorsan Bani Awad
Coronary artery disease (CAD) is the most prevalent form of cardiovascular disease that may result in myocardial infarction. Annually, it leads to millions of fatalities and causes billions of dollars in global economic losses. Limited resources and comp...
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Zhuohao Zhou, Chunyue Lu, Wenchao Wang, Wenhao Dang and Ke Gong
The training of deep neural networks usually requires a lot of high-quality data with good annotations to obtain good performance. However, in clinical medicine, obtaining high-quality marker data is laborious and expensive because it requires the profes...
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Andreea Bianca Popescu, Ioana Antonia Taca, Anamaria Vizitiu, Cosmin Ioan Nita, Constantin Suciu, Lucian Mihai Itu and Alexandru Scafa-Udriste
Deep learning (DL)-based algorithms have demonstrated remarkable results in potentially improving the performance and the efficiency of healthcare applications. Since the data typically needs to leave the healthcare facility for performing model training...
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Hong Zhang, Zhengzhen Li, Hao Zhao, Zan Li and Yanping Zhang
Medical image classification plays an essential role in disease diagnosis and clinical treatment. More and more research efforts have been dedicated to the design of effective methods for medical image classification. As an effective framework, the capsu...
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Kazuki Koga and Kazuhiro Takemoto
Universal adversarial attacks, which hinder most deep neural network (DNN) tasks using only a single perturbation called universal adversarial perturbation (UAP), are a realistic security threat to the practical application of a DNN for medical imaging. ...
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Mustafa Kara, Zeynep Öztürk, Sergin Akpek and Aysegül Turupcu
Advancements in deep learning and availability of medical imaging data have led to the use of CNN-based architectures in disease diagnostic assisted systems. In spite of the abundant use of reverse transcription-polymerase chain reaction-based tests in C...
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Kamran Kowsari, Rasoul Sali , Lubaina Ehsan, William Adorno , Asad Ali, Sean Moore, Beatrice Amadi, Paul Kelly, Sana Syed and Donald Brown
Image classification is central to the big data revolution in medicine. Improved information processing methods for diagnosis and classification of digital medical images have shown to be successful via deep learning approaches. As this field is explored...
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Mohammad Alkhaleefah, Shang-Chih Ma, Yang-Lang Chang, Bormin Huang, Praveen Kumar Chittem and Vishnu Priya Achhannagari
Differentiation between benign and malignant breast cancer cases in X-ray images can be difficult due to their similar features. In recent studies, the transfer learning technique has been used to classify benign and malignant breast cancer by fine-tunin...
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Yuhai Yu, Hongfei Lin, Jiana Meng, Xiaocong Wei, Hai Guo and Zhehuan Zhao
Medical images are valuable for clinical diagnosis and decision making. Image modality is an important primary step, as it is capable of aiding clinicians to access required medical image in retrieval systems. Traditional methods of modality classificati...
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Long Hoang, Suk-Hwan Lee, Eung-Joo Lee and Ki-Ryong Kwon
Skin lesion classification has recently attracted significant attention. Regularly, physicians take much time to analyze the skin lesions because of the high similarity between these skin lesions. An automated classification system using deep learning ca...
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Wandile Nhlapho, Marcellin Atemkeng, Yusuf Brima and Jean-Claude Ndogmo
The advent of deep learning (DL) has revolutionized medical imaging, offering unprecedented avenues for accurate disease classification and diagnosis. DL models have shown remarkable promise for classifying brain tumors from Magnetic Resonance Imaging (M...
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Domantas Kuzinkovas and Sandhya Clement
Advances in the field of image classification using convolutional neural networks (CNNs) have greatly improved the accuracy of medical image diagnosis by radiologists. Numerous research groups have applied CNN methods to diagnose respiratory illnesses fr...
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Pedro F. Durães and Mário P. Véstias
The very good results achieved with recent algorithms for image classification based on deep learning have enabled new applications in many domains. The medical field is one that can greatly benefit from these algorithms in order to help the medical prof...
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Kalifa Shantta,Otman Basir
Pág. 55 - 61
Even with the enormous progress in medical technology, brain tumor detection is still an extremely tedious and complex task for the physicians. The early and accurate detection of brain tumors enables effective and efficient therapy and thus can result i...
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Jiafu Liu, Zhiyuan Yao, Shirui Guo, Hongjun Xie and Genke Yang
Image classification is widely used in industrial defect detection, medical diagnosis, social welfare, and other fields, in which privacy and security of models and data must be involved. For example, in diamond synthesis, the diamond substrate image ann...
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Jayashree Moorthy and Usha Devi Gandhi
Deep learning techniques have rapidly become important as a preferred method for evaluating medical image segmentation. This survey analyses different contributions in the deep learning medical field, including the major common issues published in recent...
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Run Fang, Libo Zeng and Fan Yi
Multi-spectral imaging technique plays an important role in real-world applications such as medicine and medical detections. This paper proposes a cervical cancer cell screening method to simultaneously adopt TBS classification and DNA quantitative analy...
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Boris Kovalerchuk and Dmytro Dovhalets
The exploration of multidimensional datasets of all possible sizes and dimensions is a long-standing challenge in knowledge discovery, machine learning, and visualization. While multiple efficient visualization methods for n-D data analysis exist, the lo...
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Claudia Angelica Rivera-Romero, Jorge Ulises Munoz-Minjares, Carlos Lastre-Dominguez and Misael Lopez-Ramirez
Identifying patient posture while they are lying in bed is an important task in medical applications such as monitoring a patient after a surgical intervention, sleep supervision to identify behavioral and physiological markers, or for bedsore prevention...
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Eduardo Morales-Vargas, Hayde Peregrina-Barreto, Rita Q. Fuentes-Aguilar, Juan Pablo Padilla-Martinez, Wendy Argelia Garcia-Suastegui and Julio C. Ramirez-San-Juan
Microvasculature analysis is an important task in the medical field due to its various applications. It has been used for the diagnosis and threat of diseases in fields such as ophthalmology, dermatology, and neurology by measuring relative blood flow or...
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