Article
Computer Science, Interdisciplinary Applications
Hans Pinckaers, Wouter Bulten, Jeroen van der Laak, Geert Litjens
Summary: Prostate cancer is the most prevalent cancer among men in Western countries, and pathologists' evaluation is the gold standard for diagnosis. State-of-the-art convolutional neural networks are often patch-based and require detailed pixel-level annotations for effective training.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2021)
Article
Mathematics
Haixia Zheng, Yu Zhou, Xin Huang
Summary: This study proposes an effective spatially sensitive learning framework for cancer metastasis detection in whole-slide images, improving prediction consistency through a novel spatial loss function and achieving high precision and recall. By learning the spatial relationships between adjacent image patches, it provides more accurate detection results and is beneficial for early diagnosis of cancer metastasis.
Article
Oncology
Xiaobo Zhang, Wei Ba, Xiaoya Zhao, Chen Wang, Qiting Li, Yinli Zhang, Shanshan Lu, Lang Wang, Shuhao Wang, Zhigang Song, Danhua Shen
Summary: This study aims to develop a deep learning system for endometrial cancer detection using whole-slide images (WSIs). The model achieved a high degree of accuracy in identifying EC, serving as an assisted diagnostic tool to improve working efficiency for pathologists.
FRONTIERS IN ONCOLOGY
(2022)
Article
Medicine, General & Internal
Pushpanjali Gupta, Yenlin Huang, Prasan Kumar Sahoo, Jeng-Fu You, Sum-Fu Chiang, Djeane Debora Onthoni, Yih-Jong Chern, Kuo-Yu Chao, Jy-Ming Chiang, Chien-Yuh Yeh, Wen-Sy Tsai
Summary: Colorectal cancer is a major cause of cancer-related death worldwide, with early diagnosis crucial for reducing mortality and treatment burden. Automated AI-based classification and localization models are proposed to assist pathologists in accurately determining and localizing abnormal regions in whole slide images, achieving high F-scores and AUC with pretrained and customized models.
Article
Computer Science, Information Systems
Zhang Li, Jiehua Zhang, Tao Tan, Xichao Teng, Xiaoliang Sun, Hong Zhao, Lihong Liu, Yang Xiao, Byungjae Lee, Yilong Li, Qianni Zhang, Shujiao Sun, Yushan Zheng, Junyu Yan, Ni Li, Yiyu Hong, Junsu Ko, Hyun Jung, Yanling Liu, Yu-cheng Chen, Ching-wei Wang, Vladimir Yurovskiy, Pavel Maevskikh, Vahid Khanagha, Yi Jiang, Li Yu, Zhihong Liu, Daiqiang Li, Peter J. Schueffler, Qifeng Yu, Hui Chen, Yuling Tang, Geert Litjens
Summary: The ACDC@LungHP challenge evaluated various computer-aided diagnosis methods for automatic detection and classification of lung cancer in pathology slides, with a focus on deep learning techniques. The study found that multi-model methods were significantly superior to single model methods in lung cancer segmentation.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2021)
Article
Oncology
Hao Fu, Weiming Mi, Boju Pan, Yucheng Guo, Junjie Li, Rongyan Xu, Jie Zheng, Chunli Zou, Tao Zhang, Zhiyong Liang, Junzhong Zou, Hao Zou
Summary: PDAC, one of the deadliest cancer types, requires histopathology image analysis for detection and diagnosis. Manual diagnosis is time-consuming and lacks accuracy, leading to the development of deep-learning methods as an alternative to feature extraction-based classification. The proposed model in this study demonstrates high accuracy in diagnosing PDAC using histopathological images.
FRONTIERS IN ONCOLOGY
(2021)
Article
Computer Science, Artificial Intelligence
Felipe Andre Zeiser, Cristiano Andre da Costa, Gabriel de Oliveira Ramos, Henrique C. Bohn, Ismael Santos, Adriana Vial Roehe
Summary: This article introduces a model based on Convolutional Neural Networks for refined segmentation of breast cancer Whole Slide Imaging. The methodology consists of four modules designed to provide interpretable predictions for pathologists, assisting them in accurately diagnosing breast cancer cases.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Engineering, Biomedical
Yanbo Feng, Adel Hafiane, Helene Laurent
Summary: This paper introduces a multi-scale image processing method based on deep neural networks for liver cancer segmentation in Whole Slide Images. By constructing a seven-level gaussian pyramid representation, the method effectively captures texture information and produces superior results compared to state-of-the-art techniques.
COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
(2021)
Article
Chemistry, Multidisciplinary
Elena Martinez-Fernandez, Ignacio Rojas-Valenzuela, Olga Valenzuela, Ignacio Rojas
Summary: This paper analyzes the impact of parameters and hyperparameters of a deep learning architecture on the classification of colorectal cancer histopathology images. It discusses preprocessing methods for these images and proposes a new experiment based on the variation of learning rate. The results show that the triangular-cyclic learning rate optimization strategy achieves an accuracy of 96.4% on test images.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Artificial Intelligence
Wei Gao, Yangming Wu, Cui Hong, Rong-Jong Wai, Cheng-Tao Fan
Summary: This paper proposes a new bird damage recognition network, RCVNet, which addresses the issue of environmental interference in identifying birds around power towers using cameras alone by fusing radio-frequency (RF) images and visual images. The network accurately identifies bird damages in the monitoring area by employing a feature layer fusion approach and incorporating various improved modules and strategies. The experiments conducted using a newly gathered bird dataset called CRB2022 demonstrate that RCVNet achieves a high precision and recall rate in bird identification and an excellent discrimination rate in bird damage recognition.
ADVANCED ENGINEERING INFORMATICS
(2023)
Article
Computer Science, Information Systems
Yiping Jiao, Junhong Li, Shumin Fei
Summary: This study proposes an intuitive method to visualize the color style of Hematoxylin and Eosin (H&E) stained whole-slide images (WSIs) and validates its effectiveness in lung cancer research.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Ahmed S. El-Hossiny, Walid Al-Atabany, Osama Hassan, Ahmed M. Soliman, Sherif A. Sami
Summary: The research aims to build a Whole Slide Images classification system using Convolutional Neural Network (CNN) for Thyroid tumors, achieving an overall accuracy of 94.69%. The cascaded CNN technique was used for further subclassification with an accuracy of 95.90%, outperforming other studies by reducing the number of classes in each stage.
Article
Cell Biology
Hanlin Ding, Yipeng Feng, Xing Huang, Jijing Xu, Te Zhang, Yingkuan Liang, Hui Wang, Bing Chen, Qixing Mao, Wenjie Xia, Xiaocheng Huang, Lin Xu, Gaochao Dong, Feng Jiang
Summary: The study establishes a deep learning model called LSDLM for classifying histological patterns in lung adenocarcinoma (LUAD). The model shows robust performance in identifying histopathological subtypes on the whole-slide level and possesses mixed histology pattern recognition on par with senior pathologists.
Article
Oncology
Islam Alzoubi, Guoqing Bao, Rong Zhang, Christina Loh, Yuqi Zheng, Svetlana Cherepanoff, Gary Gracie, Maggie Lee, Michael Kuligowski, Kimberley L. Alexander, Michael E. Buckland, Xiuying Wang, Manuel B. Graeber
Summary: This article presents a workflow for AI-based profiling of individual cells in whole-slide scans of histological tissue sections. It demonstrates the capability of autonomously detecting and counting immunochemically labeled cells with high prediction performance, which can be used for whole-slide cross-modality analyses.
Article
Plant Sciences
Chunshan Wang, Ji Zhou, Yan Zhang, Huarui Wu, Chunjiang Zhao, Guifa Teng, Jiuxi Li
Summary: The study addressed the issue of weak robustness in disease image recognition models based on deep learning by proposing a feature decomposition and recombination method, and applying graph convolutional neural network for feature learning to build a vegetable disease recognition model based on the fusion of images and graph structure text.
FRONTIERS IN PLANT SCIENCE
(2022)
Article
Biology
Naveed Chouhan, Asifullah Khan, Jehan Zeb Shah, Mazhar Hussnain, Muhammad Waleed Khan
Summary: An automatic Diverse Features based Breast Cancer Detection system is proposed in this study, using a deep convolution neural network and two classifiers for training, which improves the system's performance.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Computer Science, Information Systems
Anum Mushtaq, Irfan ul Haq, Wajih un Nabi, Asifullah Khan, Omair Shafiq
Summary: This paper suggests using swarm intelligence in conjunction with V2V and V2I communications to maintain traffic flow during congestion. Experimental results demonstrate significant improvements in addressing congestion and collision avoidance.
Article
Biology
Saddam Hussain Khan, Anabia Sohail, Asifullah Khan, Mehdi Hassan, Yeon Soo Lee, Jamshed Alam, Abdul Basit, Saima Zubair
Summary: The study introduces two new deep learning frameworks for effective COVID-19 detection in X-ray datasets. The frameworks leverage the representation learning ability of COVIDRENet-1 & 2 models individually through a machine learning classifier in DHL, while also incorporating transfer learning on chest X-rays and generating deep feature spaces for better detection performance in DBHL.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Chemistry, Multidisciplinary
Muhammad Asam, Shaik Javeed Hussain, Mohammed Mohatram, Saddam Hussain Khan, Tauseef Jamal, Amad Zafar, Asifullah Khan, Muhammad Umair Ali, Umme Zahoora
Summary: This study proposes two new malware classification frameworks, which use deep feature space and boosted feature space to improve the performance in classifying difficult malware categories.
APPLIED SCIENCES-BASEL
(2021)
Article
Medicine, General & Internal
Saddam Hussain Khan, Anabia Sohail, Asifullah Khan, Yeon-Soo Lee
Summary: COVID-19 is a respiratory illness with devastating consequences globally. A new CNN architecture called STM-RENet is developed to interpret radiographic patterns from X-ray images, and a CB-STM-RENet is proposed to enhance the detection accuracy by exploiting channel boosting and learning textural variations.
Article
Computer Science, Artificial Intelligence
Umme Zahoora, Muttukrishnan Rajarajan, Zahoqing Pan, Asifullah Khan
Summary: This study introduces a Deep Contractive Autoencoder based Attribute Learning technique and an Inference Stage method based on Heterogeneous Voting Ensemble, which can effectively handle unseen classes and detect zero-day attacks.
APPLIED INTELLIGENCE
(2022)
Article
Oncology
Muhammad Mohsin Zafar, Zunaira Rauf, Anabia Sohail, Abdul Rehman Khan, Muhammad Obaidullah, Saddam Hussain Khan, Yeon Soo Lee, Asifullah Khan
Summary: This study presents a deep convolutional neural network based lymphocyte counter, which improves accuracy through a two-phase correction. Experimental results show its performance outperforms existing models and demonstrates good generalization ability.
PHOTODIAGNOSIS AND PHOTODYNAMIC THERAPY
(2022)
Article
Chemistry, Analytical
Mirza Mumtaz Zahoor, Shahzad Ahmad Qureshi, Sameena Bibi, Saddam Hussain Khan, Asifullah Khan, Usman Ghafoor, Muhammad Raheel Bhutta
Summary: A novel two-phase deep learning-based framework is proposed for the detection and categorization of brain tumors in MRIs. The framework achieves effective tumor detection through deep convolutional neural networks and machine learning classifiers, and categorizes different tumor types using a fusion of static and dynamic features.
Article
Physics, Multidisciplinary
Mirza Mumtaz Zahoor, Shahzad Ahmad Qureshi, Asifullah Khan, Aziz ul Rehman, Muhammad Rafique
Summary: In this study, a dual-channel brain tumor detection framework is proposed to improve the detection performance by using dynamic and static features. Computer experiments on a public brain tumor dataset show that the proposed framework outperforms other existing methods with high accuracy and F-score.
WAVES IN RANDOM AND COMPLEX MEDIA
(2022)
Article
Chemistry, Multidisciplinary
Aqsa Kiran, Shahzad Ahmad Qureshi, Asifullah Khan, Sajid Mahmood, Muhammad Idrees, Aqsa Saeed, Muhammad Assam, Mohamad Reda A. Refaai, Abdullah Mohamed
Summary: This paper proposes a new deep learning-based methodology that achieves a more efficient feature database through the collaboration of two effective models without using image metadata. Experiments show that the retrieval accuracy of this method is generally 97% under different types of noise.
APPLIED SCIENCES-BASEL
(2022)
Article
Multidisciplinary Sciences
Muhammad Asam, Saddam Hussain Khan, Altaf Akbar, Sameena Bibi, Tauseef Jamal, Asifullah Khan, Usman Ghafoor, Muhammad Raheel Bhutta
Summary: The interaction between devices, people, and the Internet has led to the emergence of IoT, introducing security challenges. This study proposes a CNN-based IoT malware detection architecture to address the malware detection challenge in IoT devices. The proposed architecture achieves promising malware detection capacity with 97.93% accuracy, demonstrating potential for extended applications in the future.
SCIENTIFIC REPORTS
(2022)
Article
Chemistry, Analytical
Anum Mushtaq, Irfan Ul Haq, Muhammad Azeem Sarwar, Asifullah Khan, Wajeeha Khalil, Muhammad Abid Mughal
Summary: Intelligent traffic management systems have gained significant attention in Intelligent Transportation Systems (ITS), and Reinforcement Learning (RL) based control methods have become increasingly popular in applications such as autonomous driving and traffic management solutions in ITS. This paper proposes an approach based on Multi-Agent Reinforcement Learning (MARL) and smart routing to improve the flow of autonomous vehicles on road networks. The evaluation of Multi-Agent Advantage Actor-Critic (MA2C) and Independent Advantage Actor-Critical (IA2C) shows the potential of these recently suggested techniques for traffic signal optimization. The effectiveness and reliability of the method are demonstrated through simulations using SUMO, a software modeling tool for traffic simulations.
Article
Computer Science, Information Systems
Faiza Babar Khan, Muhammad Hanif Durad, Asifullah Khan, Farrukh Aslam Khan, Sajjad Hussain Chauhdary, Mohammed Alqarni
Summary: Malware is a significant threat to information security, and efficient anti-malware software is crucial for protection. However, identifying malware remains challenging, especially with unknown samples. In this paper, a novel architecture based on the Relation Network is proposed for Few-Shot Learning (FSL) implementation, achieving improved classification accuracy by up to 94% with only one training instance.
Article
Computer Science, Information Systems
Muhammad Arif Arshad, Saddam Hussain Khan, Suleman Qamar, Muhammad Waleed Khan, Iqbal Murtza, Jeonghwan Gwak, Asifullah Khan
Summary: This article presents a novel strategy for drone navigation in complex and dynamic environments using a deep Convolutional Neural Network (CNN). The proposed method effectively navigates drones and helps them avoid obstacles. The experimental results show promising performance and suggest that the approach can be applied to real-time drone navigation and real-world flights.
Article
Computer Science, Information Systems
Suleman Qamar, Saddam Hussain Khan, Muhammad Arif Arshad, Maryam Qamar, Jeonghwan Gwak, Asifullah Khan
Summary: This study introduces an autonomous approach utilizing deep reinforcement learning for swarm navigation in complex environments, with the novel island policy optimization model and new reward functions for handling multiple dynamic targets to enhance swarm dynamics.
Article
Computer Science, Artificial Intelligence
Jin Zhang, Zekang Bian, Shitong Wang
Summary: This study proposes a novel style linear k-nearest neighbor method to extract stylistic features using matrix expressions and improve the generalizability of the predictor through style membership vectors.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qifeng Wan, Xuanhua Xu, Jing Han
Summary: In this study, we propose an innovative approach for dimensionality reduction in large-scale group decision-making scenarios that targets linguistic preferences. The method combines TF-IDF feature similarity and information loss entropy to address challenges in decision-making with a large number of decision makers.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Hegui Zhu, Yuchen Ren, Chong Liu, Xiaoyan Sui, Libo Zhang
Summary: This paper proposes an adversarial attack method based on frequency information, which optimizes the imperceptibility and transferability of adversarial examples in white-box and black-box scenarios respectively. Experimental results validate the superiority of the proposed method and its application in real-world online model evaluation reveals their vulnerability.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jing Tang, Xinwang Liu, Weizhong Wang
Summary: This paper proposes a hybrid generalized TODIM approach in the Fine-Kinney framework to evaluate occupational health and safety hazards. The approach integrates CRP, dynamic SIN, and PLTSs to handle opinion interactions and incomplete opinions among decision makers. The efficiency and rationality of the proposed approach are demonstrated through a numerical example, comparison, and sensitivity studies.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Shigen Shen, Chenpeng Cai, Zhenwei Li, Yizhou Shen, Guowen Wu, Shui Yu
Summary: To address the damage caused by zero-day attacks on SIoT systems, researchers propose a heuristic learning intrusion detection system named DQN-HIDS. By integrating Deep Q-Networks (DQN) into the system, DQN-HIDS gradually improves its ability to identify malicious traffic and reduces resource workloads. Experiments demonstrate the superior performance of DQN-HIDS in terms of workload, delayed sample queue, rewards, and classifier accuracy.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu
Summary: In this paper, we propose a Chinese text classification algorithm based on deep active learning for the power system, which addresses the challenge of specialized text classification. By applying a hierarchical confidence strategy, our model achieves higher classification accuracy with fewer labeled training data.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Kaan Deveci, Onder Guler
Summary: This study proves the lack of robustness in nonlinear IF distance functions for ranking intuitionistic fuzzy sets (IFS) and proposes an alternative ranking method based on hypervolume metric. Additionally, the suggested method is extended as a new multi-criteria decision making method called HEART, which is applied to evaluate Turkey's energy alternatives.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Fu-Wing Yu, Wai-Tung Ho, Chak-Fung Jeff Wong
Summary: This research aims to enhance the energy management in commercial building air-conditioning systems, specifically focusing on chillers. Ridge regression is found to outperform lasso and elastic net regression when optimized with the appropriate hyperparameter, making it the most suitable method for modeling the system coefficient of performance (SCOP). The key variables that strongly influence SCOP include part load ratios, the operating numbers of chillers and pumps, and the temperatures of chilled water and condenser water. Additionally, July is identified as the month with the highest potential for performance improvement. This study introduces a novel approach that balances feature selection, model accuracy, and optimal tuning of hyperparameters, highlighting the significance of a generic and simplified chiller system model in evaluating energy management opportunities for sustainable operation. The findings from this research can guide future efforts towards more energy-efficient and sustainable operations in commercial buildings.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Xiaoyan Chen, Yilin Sun, Qiuju Zhang, Xuesong Dai, Shen Tian, Yongxin Guo
Summary: In this study, a method for dynamically non-destructive grasping of thin-skinned fruits is proposed. It utilizes a multi-modal depth fusion convolutional neural network for image processing and segmentation, and combines the evaluation mechanism of optimal grasping stability and the forward-looking non-destructive grasp control algorithm. The proposed method greatly improves the comprehensive performance of grasping delicate fruits using flexible hands.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Yuxuan Yang, Siyuan Zhou, He Weng, Dongjing Wang, Xin Zhang, Dongjin Yu, Shuiguang Deng
Summary: The study proposes a novel model, POIGDE, which addresses the challenges of data sparsity and elusive motives by solving graph differential equations to capture continuous variation of users' interests. The model learns interest transference dynamics using a time-serial graph and an interval-aware attention mechanism, and applies Siamese learning to directly learn from label representations for predicting future POI visits. The model outperforms state-of-the-art models on real-world datasets, showing potential in the POI recommendation domain.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
S. Karthika, P. Rathika
Summary: The widespread development of monitoring devices in the power system has generated a large amount of power consumption data. Storing and transmitting this data has become a significant challenge. This paper proposes an adaptive data compression algorithm based on the discrete wavelet transform (DWT) for power system applications. It utilizes multi-objective particle swarm optimization (MO-PSO) to select the optimal threshold. The algorithm has been tested and outperforms other existing algorithms.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jiaqi Guo, Haiyan Wu, Xiaolei Chen, Weiguo Lin
Summary: In this study, an adaptive SV-Borderline SMOTE-SVM algorithm is proposed to address the challenge of imbalanced data classification. The algorithm maps the data into kernel space using SVM and identifies support vectors, then generates new samples based on the neighbors of these support vectors. Extensive experiments show that this method is more effective than other approaches in imbalanced data classification.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qiumei Zheng, Linkang Xu, Fenghua Wang, Yongqi Xu, Chao Lin, Guoqiang Zhang
Summary: This paper proposes a new semantic segmentation network model called HilbertSCNet, which combines the Hilbert curve traversal and the dual pathway idea to design a new spatial computation module to address the problem of loss of information for small targets in high-resolution images. The experiments show that the proposed network performs well in the segmentation of small targets in high-resolution maps such as drone aerial photography.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Mojtaba Ashour, Amir Mahdiyar
Summary: Analytic Hierarchy Process (AHP) is a widely applied technique in multi-criteria decision-making problems, but the sheer number of AHP methods presents challenges for scholars and practitioners in selecting the most suitable method. This paper reviews articles published between 2010 and 2023 proposing hybrid, improved, or modified AHP methods, classifies them based on their contributions, and provides a comprehensive summary table and roadmap to guide the method selection process.
APPLIED SOFT COMPUTING
(2024)
Review
Computer Science, Artificial Intelligence
Gerardo Humberto Valencia-Rivera, Maria Torcoroma Benavides-Robles, Alonso Vela Morales, Ivan Amaya, Jorge M. Cruz-Duarte, Jose Carlos Ortiz-Bayliss, Juan Gabriel Avina-Cervantes
Summary: Electric power system applications are complex optimization problems. Most literature reviews focus on studying electrical paradigms using different optimization techniques, but there is a lack of review on Metaheuristics (MHs) in these applications. Our work provides an overview of the paradigms underlying such applications and analyzes the most commonly used MHs and their search operators. We also discover a strong synergy between the Renewable Energies paradigm and other paradigms, and a significant interest in Load-Forecasting optimization problems. Based on our findings, we provide helpful recommendations for current challenges and potential research paths to support further development in this field.
APPLIED SOFT COMPUTING
(2024)
