AIMC Topic: Neural Networks, Computer

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Deep-Learning Model Prediction of Radiation Pneumonitis Using Pretreatment Chest Computed Tomography and Clinical Factors.

Technology in cancer research & treatment
This study aimed to build a comprehensive deep-learning model for the prediction of radiation pneumonitis using chest computed tomography (CT), clinical, dosimetric, and laboratory data. Radiation therapy is an effective tool for treating patients ...

An Artificial Intelligence Driven Approach for Classification of Ophthalmic Images using Convolutional Neural Network: An Experimental Study.

Current medical imaging
BACKGROUND: Early disease detection is emphasized within ophthalmology now more than ever, and as a result, clinicians and innovators turn to deep learning to expedite accurate diagnosis and mitigate treatment delay. Efforts concentrate on the creati...

Automatic Optic Nerve Assessment From Transorbital Ultrasound Images: A Deep Learning-based Approach.

Current medical imaging
BACKGROUND: Transorbital Ultrasonography (TOS) is a promising imaging technology that can be used to characterize the structures of the optic nerve and the potential alterations that may occur in those structures as a result of an increase in intracr...

Revolutionizing tumor detection and classification in multimodality imaging based on deep learning approaches: Methods, applications and limitations.

Journal of X-ray science and technology
BACKGROUND: The emergence of deep learning (DL) techniques has revolutionized tumor detection and classification in medical imaging, with multimodal medical imaging (MMI) gaining recognition for its precision in diagnosis, treatment, and progression ...

Artificial Intelligence-suggested Predictive Model of Survival in Patients Treated With Stereotactic Radiotherapy for Early Lung Cancer.

In vivo (Athens, Greece)
BACKGROUND/AIM: Overall survival (OS)-predictive models to clinically stratify patients with stage I Non-Small Cell Lung Cancer (NSCLC) undergoing stereotactic body radiation therapy (SBRT) are still unavailable. The aim of this work was to build a p...

Classification of DNA Sequence Based on a Non-gradient Algorithm: Pseudoinverse Learners.

Methods in molecular biology (Clifton, N.J.)
This chapter proposes a prototype-based classification approach for analyzing DNA barcodes that uses a spectral representation of DNA sequences and a non-gradient neural network. Biological sequences can be viewed as data components with higher non-f...

Convolutional Neural Networks for Glioma Segmentation and Prognosis: A Systematic Review.

Critical reviews in oncogenesis
Deep learning (DL) is poised to redefine the way medical images are processed and analyzed. Convolutional neural networks (CNNs), a specific type of DL architecture, are exceptional for high-throughput processing, allowing for the effective extractio...

Deep learning-based anatomical position recognition for gastroscopic examination.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: The gastroscopic examination is a preferred method for the detection of upper gastrointestinal lesions. However, gastroscopic examination has high requirements for doctors, especially for the strict position and quantity of the archived i...

IoT-based external attacks aware secure healthcare framework using blockchain and SB-RNN-NVS-FU techniques.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: In recent times, there has been widespread deployment of Internet of Things (IoT) applications, particularly in the healthcare sector, where computations involving user-specific data are carried out on cloud servers. However, the network ...

DDA-SSNets: Dual decoder attention-based semantic segmentation networks for COVID-19 infection segmentation and classification using chest X-Ray images.

Journal of X-ray science and technology
BACKGROUND: COVID-19 needs to be diagnosed and staged to be treated accurately. However, prior studies' diagnostic and staging abilities for COVID-19 infection needed to be improved. Therefore, new deep learning-based approaches are required to aid r...