Convolutional Neural Networks (CNNs) achieve strong image classification performance but lack interpretability and are vulnerable to adversarial attacks. Neuro-fuzzy hybrids such as DCNFIS replace fully connected CNN classifiers with Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to improve interpretability, yet their robustness remains underexplored. This work compares standard CNNs (ConvNet, VGG, ResNet18) with their ANFIS-augmented counterparts on MNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100 under gradient-based (PGD) and gradient-free (Square) attacks. Results show that ANFIS integration does not consistently improve clean accuracy and has architecture-dependent effects on robustness: ResNet18-ANFIS exhibits improved adversarial robustness, while VGG-ANFIS often underperforms its baseline. These findings suggest that neuro-fuzzy augmentation can enhance robustness in specific architectures but is not universally beneficial.
Detection and segmentation of Brain tumor is very important because it provides anatomical information of normal and abnormal tissues which helps in treatment planning and patient follow-up. There are number of techniques for image segmentation. Proposed research work uses ANFIS (Artificial Neural Network Fuzzy Inference System) for image classification and then compares the results with FCM (Fuzzy C means) and K-NN (K-nearest neighbor). ANFIS includes benefits of both ANN and the fuzzy logic systems. A comprehensive feature set and fuzzy rules are selected to classify an abnormal image to the…
Shahjalal University of Science and Technology（沙赫贾拉尔科技大学）；University of Bedfordshire（贝德福德大学）；University of Barishal（巴里萨尔大学）
Monkeypox is a rare disease that raised concern among medical specialists following the convi-19 pandemic. It's concerning since monkeypox is difficult to diagnose early on because of symptoms that are similar to chickenpox and measles. Furthermore, because this is a rare condition, there is a knowledge gap among healthcare professionals. As a result, there is an urgent need for a novel technique to combat and anticipate the disease in the early phases of individual virus infection. Multiple CNN-based pre-trained models, including VGG-16, VGG-19, Restnet50, Inception-V3, Densnet, Xception, Mob…
Otto von Guericke University Magdeburg（马格德堡奥托·冯·格里克大学）；German Center for Neurodegenerative Disease（德国神经退行性疾病中心）；Center for Behavioral Brain Sciences（行为脑科学中心）
Neural networks, especially convolutional neural networks (CNN), are one of the most common tools these days used in computer vision. Most of these networks work with real-valued data using real-valued features. Complex-valued convolutional neural networks (CV-CNN) can preserve the algebraic structure of complex-valued input data and have the potential to learn more complex relationships between the input and the ground-truth. Although some comparisons of CNNs and CV-CNNs for different tasks have been performed in the past, a large-scale investigation comparing different models operating on di…
The Hong Kong University of Science and Technology（香港科技大学）；The Tongji University（同济大学）
Convolutional neural networks (CNNs) are widely used in many image recognition tasks due to their extraordinary performance. However, training a good CNN model can still be a challenging task. In a training process, a CNN model typically learns a large number of parameters over time, which usually results in different performance. Often, it is difficult to explore the relationships between the learned parameters and the model performance due to a large number of parameters and different random initializations. In this paper, we present a visual analytics approach to compare two different snaps…
Understanding actions and gestures in video streams requires temporal reasoning of the spatial content from different time instants, i.e., spatiotemporal (ST) modeling. In this survey paper, we have made a comparative analysis of different ST modeling techniques for action and gecture recognition tasks. Since Convolutional Neural Networks (CNNs) are proved to be an effective tool as a feature extractor for static images, we apply ST modeling techniques on the features of static images from different time instants extracted by CNNs. All techniques are trained end-to-end together with a CNN feat…
TU Dresden（德累斯顿工业大学）；AICURA medical GmbH（AICURA医疗有限公司）；National Center of Tumor Diseases (NCT)（国家肿瘤疾病中心）；Max Planck Institute for Human Cognitive and Brain Sciences（马克斯·普朗克人类认知与脑科学研究所）
In recent years, Convolutional Neural Networks (CNNs) have become the state-of-the-art method for biomedical image analysis. However, these networks are usually trained in a supervised manner, requiring large amounts of labelled training data. These labelled data sets are often difficult to acquire in the biomedical domain. In this work, we validate alternative ways to train CNNs with fewer labels for biomedical image segmentation using. We adapt two semi- and self-supervised image classification methods and analyse their performance for semantic segmentation of biomedical microscopy images.
Deep Network Pruning: A Comparative Study on CNNs in Face Recognition
School of Information Science, Computer and Electrical Engineering, Halmstad University（信息科学、计算机与电气工程学院，哈姆斯塔德大学）；Computer Graphics and Vision and AI Group, University of Balearic Islands（计算机图形学与视觉与人工智能小组，巴利阿里大学）
A Comparative Study of Vision Transformers and CNNs for Few-Shot Rigid Transformation and Fundamental Matrix Estimation
Department of Computer Science, Ben-Gurion University of the Negev（本·古里安大学内盖夫分校计算机科学系）；School of Electrical and Computer Engineering, Ben-Gurion University of the Negev（本·古里安大学内盖夫分校电气与计算机工程学院）；GE Healthcare Science and Technology（通用电气医疗科技）
Waste is a wealth in a wrong place. Our research focuses on analyzing possibilities for automatic waste sorting and collecting in such a way that helps it for further recycling process. Various approaches are being practiced managing waste but not efficient and require human intervention. The automatic waste segregation would fit in to fill the gap. The project tested well known Deep Learning Network architectures for waste classification with dataset combined from own endeavors and Trash Net. The convolutional neural network is used for image classification. The hardware built in the form of …
University of Waterloo（滑铁卢大学）；Amirkabir University of Technology（阿米尔卡比尔理工大学）；Nanjing University of Science & Technology（南京理工大学）
Despite the progress made in the field of medical imaging, it remains a large area of open research, especially due to the variety of imaging modalities and disease-specific characteristics. This paper is a comparative study describing the potential of using local binary patterns (LBP), deep features and the bag-of-visual words (BoVW) scheme for the classification of histopathological images. We introduce a new dataset, \emph{KIMIA Path960}, that contains 960 histopathology images belonging to 20 different classes (different tissue types). We make this dataset publicly available. The small siz…
Comparative Study of Anatomical and Learned Features in AI Models for Structural Brain MRI
Center for Data Science, New York University（纽约大学数据科学中心）；NYU Grossman School of Medicine（纽约大学格罗斯曼医学院）；Courant Institute of Mathematical Sciences, NYU（纽约大学柯朗数学科学研究所）
A Comparative Evaluation of Deep Learning Object Detection Models on a Real-World Multi-Plant Dataset from Africa
Bayero University（巴耶罗大学）；Alpen-Adria-Universität Klagenfurt（克拉根福阿尔卑斯-亚得里亚大学）；Gombe State University（贡贝州立大学）；Aliko Dangote University of Science and Technology（阿里科·丹格特科技大学）；Ahmadu Bello University（艾哈迈杜·贝洛大学）；Federal University Dutse（杜塞联邦大学）
National Institute of Technology, Silchar（锡尔卡尔国家理工学院）
Aided by recent advances in Deep Learning, Image Caption Generation has seen tremendous progress over the last few years. Most methods use transfer learning to extract visual information, in the form of image features, with the help of pre-trained Convolutional Neural Network models followed by transformation of the visual information using a Caption Generator module to generate the output sentences. Different methods have used different Convolutional Neural Network Architectures and, to the best of our knowledge, there is no systematic study which compares the relative efficacy of different C…
Comparative Evaluation of CNN Architectures for Neural Style Transfer in Indonesian Batik Motif Generation: A Comprehensive Study
Department of Data Science, Telkom University, Purwokerto Campus, Indonesia（数据科学系，电信大学，普尔沃克尔托校区，印度尼西亚）；Department of Informatics Engineering, Telkom University, Purwokerto Campus, Indonesia（信息技术工程系，电信大学，普尔沃克尔托校区，印度尼西亚）；Center of Excellece Human Centric Engineering, Telkom University, Indonesia（以人为本工程研究中心，电信大学，印度尼西亚）
Enhancing Fingerprint Recognition Systems: Comparative Analysis of Biometric Authentication Algorithms and Techniques for Improved Accuracy and Reliability
Breast Cancer Detection and Diagnosis: A comparative study of state-of-the-arts deep learning architectures
School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal（夸祖鲁-纳塔尔大学数学、统计与计算机科学学院）；Unit for Data Science and Computing, North-West University（西北大学数据科学与计算单元）
F. Hoffmann-La Roche AG（罗氏制药）；Genentech, Inc.（基因泰克公司）
Recently, vision transformers were shown to be capable of outperforming convolutional neural networks when pretrained on sufficient amounts of data. In comparison to convolutional neural networks, vision transformers have a weaker inductive bias and therefore allow a more flexible feature detection. Due to their promising feature detection, this work explores vision transformers for tumor detection in digital pathology whole slide images in four tissue types, and for tissue type identification. We compared the patch-wise classification performance of the vision transformer DeiT-Tiny to the sta…
The Coronavirus was first emerged in December, in the city of China named Wuhan in 2019 and spread quickly all over the world. It has very harmful effects all over the global economy, education, social, daily living and general health of humans. To restrict the quick expansion of the disease initially, main difficulty is to explore the positive corona patients as quickly as possible. As there are no automatic tool kits accessible the requirement for supplementary diagnostic tools has risen up. Previous studies have findings acquired from radiological techniques proposed that this kind of image…
To improve segmentation performance, a novel neural network architecture (termed DFCN-DCRF) is proposed, which combines an RGB-D fully convolutional neural network (DFCN) with a depth-sensitive fully-connected conditional random field (DCRF). First, a DFCN architecture which fuses depth information into the early layers and applies dilated convolution for later contextual reasoning is designed. Then, a depth-sensitive fully-connected conditional random field (DCRF) is proposed and combined with the previous DFCN to refine the preliminary result. Comparative experiments show that the proposed D…
Comparative and Interpretative Analysis of CNN and Transformer Models in Predicting Wildfire Spread Using Remote Sensing Data
Imperial College London（帝国理工学院）；University of Cambridge（剑桥大学）；University of Waterloo（滑铁卢大学）；Institut Polytechnique de Paris（巴黎综合理工学院）；EDF R&D（法国电力公司研发部门）；CEREA；ENPC（巴黎路桥学院）
