Many state-of-the-art technologies developed in recent years have been influenced by machine learning to some extent. Most popular at the time of this writing are artificial intelligence methodologies that fall under the umbrella of deep learning. Deep learning has been shown across many applications to be extremely powerful and capable of handling problems that possess great complexity and difficulty. In this work, we introduce a new layer to deep learning: the fuzzy layer. Traditionally, the network architecture of neural networks is composed of an input layer, some combination of hidden layers, and an output layer. We propose the introduction of fuzzy layers into the deep learning architecture to exploit the powerful aggregation properties expressed through fuzzy methodologies, such as the Choquet and Sugueno fuzzy integrals. To date, fuzzy approaches taken to deep learning have been through the application of various fusion strategies at the decision level to aggregate outputs from state-of-the-art pre-trained models, e.g., AlexNet, VGG16, GoogLeNet, Inception-v3, ResNet-18, etc. While these strategies have been shown to improve accuracy performance for image classification tasks, none have explored the use of fuzzified intermediate, or hidden, layers. Herein, we present a new deep learning strategy that incorporates fuzzy strategies into the deep learning architecture focused on the application of semantic segmentation using per-pixel classification. Experiments are conducted on a benchmark data set as well as a data set collected via an unmanned aerial system at a U.S. Army test site for the task of automatic road segmentation, and preliminary results are promising.
AI-Powered Intracranial Hemorrhage Detection: A Co-Scale Convolutional Attention Model with Uncertainty-Based Fuzzy Integral Operator and Feature Screening
School of Electrical and Computer Engineering, College of Engineering, University of Tehran（塔里斯坦大学电气与计算机工程学院）；Department of Computer Science and Engineering, Sejong University（世宗大学计算机科学与工程系）；Department of Neurosurgery, Iran University of Medical Sciences（伊朗医学科学大学神经外科系）
Improved Brain Tumor Detection in MRI: Fuzzy Sigmoid Convolution in Deep Learning
Turku PET Center, Turku University Hospital, University of Turku, Finland（图尔库PET中心，图尔库大学医院，图尔库大学，芬兰）；Turku Intelligent Embedded and Robotic Systems lab, University of Turku, Finland（图尔库智能嵌入式与机器人系统实验室，图尔库大学，芬兰）；School of Information Science and Technology, Fudan University, China（信息科学与技术学院，复旦大学，中国）；Information Sciences and Technology, University at Albany, USA（信息科学与技术，阿尔巴尼大学，美国）；University of Sydney, Australia（悉尼大学，澳大利亚）
Enhancing Diagnostic Precision in Gastric Bleeding through Automated Lesion Segmentation: A Deep DuS-KFCM Approach
University of Macau（澳门大学）；Guangdong Institute of Intelligence Science and Technology（广东省智能科学与技术研究院）；The Chinese University of Hong Kong（香港中文大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Chongqing Technology and Business University（重庆工商大学）；Institute of Artificial Intelligence, Chongqing Technology and Business University（重庆工商大学人工智能研究院）；Hebei University of Engineering（河北工程大学）
