4.6 Article

Transformer based on the prediction of psoriasis severity treatment response

Related references

Note: Only part of the references are listed.
Article Computer Science, Artificial Intelligence

Benchmarking of Machine Learning classifiers on plasma proteomic for COVID-19 severity prediction through interpretable artificial intelligence

Stella Dimitsaki et al.

Summary: This article evaluates a set of machine learning algorithms for predicting the severity of COVID-19 patients based on plasma proteomics and clinical data. The use of an ensemble of ML algorithms is designed and deployed for early patient triage. The evaluation shows that MLP and SVM algorithms perform the best.

ARTIFICIAL INTELLIGENCE IN MEDICINE (2023)

Article Mathematics, Interdisciplinary Applications

Overcoming Nonlinear Dynamics in Diabetic Retinopathy Classification: A Robust AI-Based Model with Chaotic Swarm Intelligence Optimization and Recurrent Long Short-Term Memory

Yusuf Bahri Ozcelik et al.

Summary: Diabetic retinopathy, affecting approximately one-third of diabetes patients worldwide, can lead to irreversible vision loss and even blindness if not diagnosed and treated in time. This paper presents an artificial intelligence-based model that uses fundus images to determine the phase of diabetic retinopathy with low computational complexity and high classification accuracy.

FRACTAL AND FRACTIONAL (2023)

Article Multidisciplinary Sciences

Psoriasis severity classification based on adaptive multi-scale features for multi-severity disease

Cho-I. Moon et al.

Summary: This study proposed a novel method for evaluating psoriasis, which improves the evaluation performance of various types of psoriasis, including multiple-severity diseases, by detecting representative regions and extracting severity features. The results showed that EfficientNet B1 with MS-DAM exhibited the best classification performance, with over 5% higher accuracy than six existing self-attention methods. Using the gradient-weighted activation mapping method, it was confirmed that the proposed method works at par with human visual perception.

SCIENTIFIC REPORTS (2023)

Article Dermatology

Estimation error of the body surface area in psoriasis: a comparative study of physician and computer-assisted image analysis (ImageJ)

Kwang Ho Yoo et al.

Summary: In psoriasis, physicians tend to overestimate the body surface area (BSA) of lesions, resulting in low accuracy and consistency. This study found that the largest estimation error occurred when assessing grade 3 (30%-49% involvement), and the second half of the range had a higher proportion of inaccuracies compared to the first half. The inaccuracy of BSA estimation by physicians may be due to the perception of information from the human eye as being exaggerated compared to the actual size. Further research using artificial intelligence technology is needed to reduce quantification error and develop an ideal BSA assessment system.

CLINICAL AND EXPERIMENTAL DERMATOLOGY (2022)

Article Computer Science, Artificial Intelligence

A novel lifelong machine learning-based method to eliminate calibration drift in clinical prediction models

Shengqiang Chi et al.

Summary: This paper proposes a model updating method based on lifelong machine learning to solve the calibration drift issue caused by data drift. The effectiveness of the proposed method is verified in four tumor datasets and it outperforms other model updating methods in improving model performance.

ARTIFICIAL INTELLIGENCE IN MEDICINE (2022)

Article Computer Science, Information Systems

Deep features to detect pulmonary abnormalities in chest X-rays due to infectious diseaseX: Covid-19, pneumonia, and tuberculosis

Milon Biswas et al.

Summary: Chest X-ray (CXR) imaging is a low-cost and easy-to-use method for diagnosing/screening pulmonary abnormalities caused by infectious diseases. A lightweight deep neural network (DNN) proposed in the study shows high accuracy in non-healthy versus healthy CXR screening, comparable to current state-of-the-art techniques.

INFORMATION SCIENCES (2022)

Article Health Care Sciences & Services

Tracing and Forecasting Metabolic Indices of Cancer Patients Using Patient-Specific Deep Learning Models

Jianguo Hou et al.

Summary: We developed a patient-specific dynamical system model for tracking and predicting patients' metabolic indices. The model, which consists of stacked LSTM recurrent neural networks and fully connected neural networks, showed high accuracy in short-term predictions.

JOURNAL OF PERSONALIZED MEDICINE (2022)

Article Economics

COVID-19: Forecasting confirmed cases and deaths with a simple time series model

Fotios Petropoulos et al.

Summary: Forecasting the outcome of outbreaks is crucial for decision-making, but the severity and the socioeconomic consequences of outbreaks are difficult to predict. This paper presents a statistical time series approach to model and predict the short-term behavior of COVID-19, which offers competitive forecast accuracy and estimates of uncertainty.

INTERNATIONAL JOURNAL OF FORECASTING (2022)

Article Engineering, Biomedical

Ensemble of weighted deep concatenated features for the skin disease classification model using modified long short term memory

Mohamed A. Elashiri et al.

Summary: Skin diseases are common and can have negative impacts on self-confidence and mental health. They may even lead to skin cancer. Implementing a computer-aided detection system using deep learning methods can improve the accuracy of skin disease diagnosis.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2022)

Article Engineering, Biomedical

Multi-channel content based image retrieval method for skin diseases using similarity network fusion and deep community analysis

Yuheng Wang et al.

Summary: This paper proposes a CBIR framework for skin diseases that incorporates multi-sourced information, including dermoscopic images, clinical images, and meta information. The framework solves dimensional bias problems for image and non-image information and improves the retrieval performance of similar images through a graph-based community analysis algorithm.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2022)

Article Engineering, Biomedical

Enhanced deep bottleneck transformer model for skin lesion classification*

Katsuhiro Nakai et al.

Summary: By developing a novel deep model to enhance skin lesion recognition performance, this study effectively increases patient's survival rate. Experimental results demonstrate that the proposed model achieves superior recognition performance on two skin lesion datasets.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2022)

Article Engineering, Biomedical

Deep feature extraction based brain image classification model using preprocessed images: PDRNet

Burak Tasci et al.

Summary: This research proposes a hybrid deep feature-based feature engineering model for stroke classification. By applying multiple preprocessing algorithms and support vector machine classifiers, high accuracy stroke classification has been achieved.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2022)

Article Computer Science, Hardware & Architecture

Classification of Breast Tumors Based on Histopathology Images Using Deep Features and Ensemble of Gradient Boosting Methods

Mohammad Reza Abbasniya et al.

Summary: This paper introduces a computer-aided diagnosis system for breast cancer using histopathology and deep feature transfer learning. Experimental results show that Inception-ResNet-v2 network exhibits the best feature extraction capability in breast cancer histopathology images, and the ensemble of CatBoost, XGBoost, and LightGBM achieves the highest accuracy in the classification phase.

COMPUTERS & ELECTRICAL ENGINEERING (2022)

Article Multidisciplinary Sciences

Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging

Lin Lu et al.

Summary: Evaluation of tumor response to anti-vascular endothelial growth factor therapies in metastatic colorectal cancer (mCRC) is limited due to morphological changes potentially being more critical than size changes. Utilizing a deep learning network, this study demonstrates improved prediction of early treatment response in mCRC patients compared to size-based methods. Integration of deep learning with size-based methodologies further enhances prediction performance.

NATURE COMMUNICATIONS (2021)

Article Computer Science, Software Engineering

Shadow removal via dual module network and low error shadow dataset

Wen Wu et al.

Summary: Shadow removal is a challenging task that requires recovering common penumbra at the shadow boundary without altering the illumination of non-shadow regions. This study proposes a novel strategy using Generative Adversarial Networks and a dual-module architecture (DM-GAN) to achieve effective shadow removal.

COMPUTERS & GRAPHICS-UK (2021)

Article Biology

An integrated autoencoder-based hybrid CNN-LSTM model for COVID-19 severity prediction from lung ultrasound

Ankan Ghosh Dastider et al.

Summary: This paper proposes a disease severity prediction architecture based on Lung Ultrasound by integrating deep convolutional and recurrent neural networks to consider spatial and temporal features of LUS frames, significantly improving the classification performance of COVID-19 severity scores.

COMPUTERS IN BIOLOGY AND MEDICINE (2021)

Article Computer Science, Artificial Intelligence

Multi-feature representation for burn depth classification via burn images

Bob Zhang et al.

Summary: Burns are a common and severe problem in public health. Early and timely classification of burn depth is effective for targeted treatment. Given the high workload and cost for clinicians, implementing smart burn depth classification methods, such as the proposed computerized method using multiple features, can significantly improve the accuracy and speed of diagnosing burn depth, outperforming conventional methods.

ARTIFICIAL INTELLIGENCE IN MEDICINE (2021)

Article Engineering, Biomedical

Evaluation of deep learning approaches for identification of different corona-virus species and time series prediction

Mohammed Chachan Younis

Summary: The research employs various convolutional neural network models for the detection of COVID-19 and SARS_MERS infected patients, with the VGG1 model showing the highest accuracy. Additionally, the LSTM model accurately predicts the number of COVID-19 cases in Italy in the next 10 days with a 99% accuracy.

COMPUTERIZED MEDICAL IMAGING AND GRAPHICS (2021)

Article Multidisciplinary Sciences

Optimization of psoriasis assessment system based on patch images

Cho-I. Moon et al.

Summary: The study proposed optimal techniques for evaluating the severity of psoriasis, established a new dataset for psoriasis, improved generalization performance of diagnosis and evaluation, suggested the best system with specific evaluation indicators and a quantitative scoring method, and can assess the severity of psoriasis more accurately.

SCIENTIFIC REPORTS (2021)

Proceedings Paper Computer Science, Artificial Intelligence

A Transformer-based Framework for Multivariate Time Series Representation Learning

George Zerveas et al.

Summary: A novel framework for multivariate time series representation learning based on transformer encoder architecture is proposed, including an unsupervised pre-training scheme that outperforms currently available methods, leading to improved performance in regression and classification tasks, even with datasets containing few samples.

KDD '21: PROCEEDINGS OF THE 27TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING (2021)

Article Biochemistry & Molecular Biology

Prediction of Skin Disease Using Ensemble Data Mining Techniques and Feature Selection Method-a Comparative Study

Anurag Kumar Verma et al.

APPLIED BIOCHEMISTRY AND BIOTECHNOLOGY (2020)

Review Dermatology

Real-world evidence of secukinumab in psoriasis treatment - a meta-analysis of 43 studies

M. Augustin et al.

JOURNAL OF THE EUROPEAN ACADEMY OF DERMATOLOGY AND VENEREOLOGY (2020)

Article Multidisciplinary Sciences

Neutrophil extracellular trap-associated RNA and LL37 enable self-amplifying inflammation in psoriasis

Franziska Herster et al.

NATURE COMMUNICATIONS (2020)

Article Computer Science, Information Systems

Automatic Scale Severity Assessment Method in Psoriasis Skin Images Using Local Descriptors

Yasmeen George et al.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2020)

Article Allergy

Personalized prediction of daily eczema severity scores using a mechanistic machine learning model

Guillem Hurault et al.

CLINICAL AND EXPERIMENTAL ALLERGY (2020)

Article Computer Science, Artificial Intelligence

InceptionTime: Finding AlexNet for time series classification

Hassan Ismail Fawaz et al.

DATA MINING AND KNOWLEDGE DISCOVERY (2020)

Article Dermatology

17850 Predicting the long-term outcomes of biologics in psoriasis patients using machine learning

Philip Surmanowicz et al.

JOURNAL OF THE AMERICAN ACADEMY OF DERMATOLOGY (2020)

Article Computer Science, Artificial Intelligence

Fine-Tuning CNN Image Retrieval with No Human Annotation

Filip Radenovic et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2019)

Article Computer Science, Information Systems

An Improved Skin Lesion Matching Scheme in Total Body Photography

Konstantin Korotkov et al.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2019)

Article Multidisciplinary Sciences

IL-17+ CD8+ T cell suppression by dimethyl fumarate associates with clinical response in multiple sclerosis

Christina Lueckel et al.

NATURE COMMUNICATIONS (2019)

Article Health Care Sciences & Services

Perspective on Living With a Skin Condition and its Psychological Impact: A Survey

A. Kanji

JOURNAL OF PATIENT EXPERIENCE (2019)

Article Computer Science, Interdisciplinary Applications

Psoriasis skin biopsy image segmentation using Deep Convolutional Neural Network

Anabik Pal et al.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2018)

Proceedings Paper Computer Science, Theory & Methods

A New Shadow Removal Method Using Color-Lines

Xiaoming Yu et al.

COMPUTER ANALYSIS OF IMAGES AND PATTERNS: 17TH INTERNATIONAL CONFERENCE, CAIP 2017, PT II (2017)

Proceedings Paper Computer Science, Artificial Intelligence

Xception: Deep Learning with Depthwise Separable Convolutions

Francois Chollet

30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) (2017)