Article
Chemistry, Analytical
Chan-Il Kim, Seok-Min Hwang, Eun-Bin Park, Chang-Hee Won, Jong-Ha Lee
Summary: The study proposed a computer-aided diagnostic algorithm for the classification of malignant melanoma and benign skin tumors using deep learning techniques. By employing U-Net model and convolutional neural networks, the algorithm achieved high accuracy in skin lesion classification.
Article
Computer Science, Artificial Intelligence
Mosleh Hmoud Al-Adhaileh
Summary: This research applied deep learning techniques, specifically AlexNet and Restnet50, for the classification and recognition of Alzheimer's disease (AD) using brain MRI images. The proposed method outperformed existing systems in terms of detection accuracy, with the AlexNet model achieving outstanding performance based on five evaluation metrics.
Article
Computer Science, Information Systems
Aya Gamal, Mustafa Elattar, Sahar Selim
Summary: This study proposes a new method for early detection of Alzheimer's disease using a computer-aided system. By processing MRI images and conducting multiple experiments, an ensemble learning approach is introduced, which outperforms previous studies in distinguishing different disease stages and multi-class tasks.
Article
Computer Science, Information Systems
Phong Thanh Nguyen, Vy Dang Bich Huynh, Khoa Dang Vo, Phuong Thanh Phan, Eunmok Yang, Gyanendra Prasad Joshi
Summary: The paper introduces an ensemble model EOPSO-CNN based on OPSO algorithm for DR detection and grading, achieving high performance in accuracy, sensitivity, and specificity. Through preprocessing, feature extraction, and classification processes, the proposed model effectively detects and grades DR.
CMC-COMPUTERS MATERIALS & CONTINUA
(2021)
Article
Computer Science, Information Systems
Hemanta Kumar Bhuyan, A. Vijayaraj, Vinayakumar Ravi
Summary: This paper discusses a single setting framework for the diagnosis system of cancer disease. It focuses on utilizing a convolutional neural network (CNN) architecture with deep learning approaches to determine the relevant illness of patients through affected images. The proposed model outperforms existing models in terms of accuracy and area under the curve (AUC), demonstrating its effectiveness in conventional detection, segmentation, and classification methods. The suggested diagnostics method can provide support for radiologists in each stage of image processing of the infected region.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Elena Pasini, Dario Genovesi, Carlo Rossi, Lisa Anita De Santi, Vincenzo Positano, Assuero Giorgetti, Maria Filomena Santarelli
Summary: This study aims to use deep learning models to automatically segment diagnostic regions related to Alzheimer's disease in PET scans to provide objective diagnosis and reduce manual segmentation variability. Results show that the U-Net3D network performs the best in segmenting brain regions.
Review
Computer Science, Artificial Intelligence
Hongfeng Li, Yini Pan, Jie Zhao, Li Zhang
Summary: This paper reviews deep learning methods and their applications in skin disease diagnosis. It introduces skin diseases and image acquisition methods, lists publicly available skin datasets, and discusses the concept of deep learning, popular architectures, frameworks, and performance evaluation metrics.
Article
Computer Science, Information Systems
Zhao Pei, Yuanshuai Gou, Miao Ma, Min Guo, Chengcai Leng, Yuli Chen, Jun Li
Summary: A novel convolutional neural network framework has been proposed for identifying Alzheimer's disease and mild cognitive impairment, incorporating pseudo-3D blocks and expanded global context blocks into residual blocks to enhance classification accuracy.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Automation & Control Systems
Siqi Cai, Yizhi Liao, Lixuan Lai, Haiyu Zhou, Longhan Xie
Summary: This article introduces a computer-aided diagnosis method based on convolutional neural networks for generating corrective solutions for patients with pectus excavatum. By training a CNN model to predict the corrected sternum contours for patients, the effectiveness of the approach was validated through experiments.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Ruizhi Han, Zhulin Liu, C. L. Philip Chen
Summary: This paper presents a new variant model of the Broad Learning System (BLS) for accurate diagnosis of Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) using MRI images. The proposed model integrates multi-scale convolution features and abstract features to achieve precise diagnosis. Experimental results demonstrate that the proposed model outperforms other methods in AD and MCI diagnostic tasks.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Interdisciplinary Applications
Xuejiao Pang, Zijian Zhao, Yanbing Wu, Yong Chen, Jin Liu
Summary: This paper proposes a transformer and convolutional neural network-based CAD system (TransMSF) that assists endoscopists in diagnosing multiple GI diseases, with superior performance compared to other state-of-the-art models and seasoned endoscopists.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2023)
Article
Mathematics, Interdisciplinary Applications
Irfan Ullah Khan, Nida Aslam, Talha Anwar, Sumayh S. Aljameel, Mohib Ullah, Rafiullah Khan, Abdul Rehman, Nadeem Akhtar
Summary: The study proposed an automated skin cancer diagnosis and triaging model by integrating clinical features, achieving improved accuracy using ensemble learning framework and data augmentation to handle imbalanced data. The integration of clinical data with skin lesions enhanced automated diagnosis accuracy, outperforming previous results for the PAD-UFES-20 data set.
Article
Radiology, Nuclear Medicine & Medical Imaging
Rafael Silva Del Lama, Raquel Mariana Candido, Natalia Santana Chiari-Correia, Marcello Henrique Nogueira-Barbosa, Paulo Mazzoncini de Azevedo-Marques, Renato Tinos
Summary: Vertebral Compression Fracture (VCF) is a condition where the vertebral body partially collapses under compressive forces. A hybrid method using convolutional neural networks and radiomics shows improved classification performance for VCFs.
JOURNAL OF DIGITAL IMAGING
(2022)
Article
Automation & Control Systems
Pakize Erdogmus, Abdullah Talha Kabakus
Summary: Alzheimer's Disease is a devastating neurologic disorder with no cure, and its symptoms eventually interfere with daily tasks. We propose a novel Convolutional Neural Network as a cheap, fast, yet accurate solution for early diagnosis, achieving an accuracy of 90.4% which outperforms existing classifiers.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Physics, Multidisciplinary
Jiu-Cheng Xie, Yanyan Gan, Ping Liang, Rushi Lan, Hao Gao
Summary: This article proposes a Parkinson's auxiliary diagnosis system based on human speech, which can adaptively build a suitable deep neural network based on sound features. Experimental results show that this method improves the accuracy of voice-based Parkinson's disease detection to some extent.
FRONTIERS IN PHYSICS
(2022)