AIMC Topic: Deep Learning

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An optimistic firefly algorithm-based deep learning approach for sentiment analysis of COVID-19 tweets.

Mathematical biosciences and engineering : MBE
The unprecedented rise in the number of COVID-19 cases has drawn global attention, as it has caused an adverse impact on the lives of people all over the world. As of December 31, 2021, more than 2, 86, 901, 222 people have been infected with COVID-1...

Unsupervised Learning Composite Network to Reduce Training Cost of Deep Learning Model for Colorectal Cancer Diagnosis.

IEEE journal of translational engineering in health and medicine
Deep learning facilitates complex medical data analysis and is increasingly being explored in colorectal cancer diagnostics. However, the training cost of the deep learning model limits its real-world medical utility. In this study, we present a comp...

Fisheye transformation enhances deep-learning-based single-cell phenotyping by including cellular microenvironment.

Cell reports methods
Incorporating information about the surroundings can have a significant impact on successfully determining the class of an object. This is of particular interest when determining the phenotypes of cells, for example, in the context of high-throughput...

Pediatric Automatic Sleep Staging: A Comparative Study of State-of-the-Art Deep Learning Methods.

IEEE transactions on bio-medical engineering
BACKGROUND: Despite the tremendous prog- ress recently made towards automatic sleep staging in adults, it is currently unknown if the most advanced algorithms generalize to the pediatric population, which displays distinctive characteristics in overn...

Vehicular Environment Identification Based on Channel State Information and Deep Learning.

Sensors (Basel, Switzerland)
This paper presents a novel vehicular environment identification approach based on deep learning. It consists of exploiting the vehicular wireless channel characteristics in the form of Channel State Information (CSI) in the receiver side of a connec...

Deep Learning-Based Computer-Aided Diagnosis (CAD): Applications for Medical Image Datasets.

Sensors (Basel, Switzerland)
Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possib...

Enhanced Tooth Region Detection Using Pretrained Deep Learning Models.

International journal of environmental research and public health
The rapid development of artificial intelligence (AI) has led to the emergence of many new technologies in the healthcare industry. In dentistry, the patient's panoramic radiographic or cone beam computed tomography (CBCT) images are used for implant...

An integrated deep learning model for the prediction of pathological complete response to neoadjuvant chemotherapy with serial ultrasonography in breast cancer patients: a multicentre, retrospective study.

Breast cancer research : BCR
BACKGROUND: The biological phenotype of tumours evolves during neoadjuvant chemotherapy (NAC). Accurate prediction of pathological complete response (pCR) to NAC in the early-stage or posttreatment can optimize treatment strategies or improve the bre...

DeepPROTACs is a deep learning-based targeted degradation predictor for PROTACs.

Nature communications
The rational design of PROTACs is difficult due to their obscure structure-activity relationship. This study introduces a deep neural network model - DeepPROTACs to help design potent PROTACs molecules. It can predict the degradation capacity of a pr...

Deep Learning Prediction of Pathologic Complete Response in Breast Cancer Using MRI and Other Clinical Data: A Systematic Review.

Tomography (Ann Arbor, Mich.)
Breast cancer patients who have pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) are more likely to have better clinical outcomes. The ability to predict which patient will respond to NAC early in the treatment course is importa...