AIMC Topic: Deep Learning

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Advanced pathological subtype classification of thyroid cancer using efficientNetB0.

Diagnostic pathology
BACKGROUND: Thyroid cancer is a prevalent malignancy requiring accurate subtype identification for effective treatment planning and prognosis evaluation. Deep learning has emerged as a valuable tool for analyzing tumor microenvironment features and d...

Accuracy of artificial intelligence-based segmentation in maxillofacial structures: a systematic review.

BMC oral health
OBJECTIVE: The aim of this review was to evaluate the accuracy of artificial intelligence (AI) in the segmentation of teeth, jawbone (maxilla, mandible with temporomandibular joint), and mandibular (inferior alveolar) canal in CBCT and CT scans.

Continuous nursing symptom management in cancer chemotherapy patients using deep learning.

Scientific reports
To assess the efficacy of a deep learning platform for managing symptoms in chemotherapy patients, aiming to enhance their quality of life. A non-randomized controlled trial was conducted from September 2022 to March 2024, involving 144 chemotherapy ...

Hybrid neural network method for damage localization in structural health monitoring.

Scientific reports
The detection of cracks in large structures is of critical importance, as such damage can result not only in significant financial costs but also pose serious risks to public safety. Many existing methods for crack detection rely on deep learning alg...

Leveraging YOLO deep learning models to enhance plant disease identification.

Scientific reports
Early automation in identifying plant diseases is crucial for the precise protection of crops. Plant diseases pose substantial risks to agriculture-dependent nations, often leading to notable crop losses and financial challenges, particularly in deve...

Diagnostic Performance of Artificial Intelligence-Based Methods for Tuberculosis Detection: Systematic Review.

Journal of medical Internet research
BACKGROUND: Tuberculosis (TB) remains a significant health concern, contributing to the highest mortality among infectious diseases worldwide. However, none of the various TB diagnostic tools introduced is deemed sufficient on its own for the diagnos...

A deep-learning model for quantifying circulating tumour DNA from the density distribution of DNA-fragment lengths.

Nature biomedical engineering
The quantification of circulating tumour DNA (ctDNA) in blood enables non-invasive surveillance of cancer progression. Here we show that a deep-learning model can accurately quantify ctDNA from the density distribution of cell-free DNA-fragment lengt...

The More, the Better? Evaluating the Role of EEG Preprocessing for Deep Learning Applications.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
The last decade has witnessed a notable surge in deep learning applications for electroencephalography (EEG) data analysis, showing promising improvements over conventional statistical techniques. However, deep learning models can underperform if tra...

DeepOptimalNet: optimized deep learning model for early diagnosis of pancreatic tumor classification in CT imaging.

Abdominal radiology (New York)
Computed Tomography (CT) imaging captures detailed cross-sectional images of the pancreas and surrounding structures and provides valuable information for medical professionals. The classification of pancreatic CT images presents significant challeng...

Enhanced ISUP grade prediction in prostate cancer using multi-center radiomics data.

Abdominal radiology (New York)
BACKGROUND: To explore the predictive value of radiomics features extracted from anatomical ROIs in differentiating the International Society of Urological Pathology (ISUP) grading in prostate cancer patients.