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Unveiling the power of convolutional neural networks in melanoma diagnosis
BackgroundConvolutional neural networks are a type of deep learning algorithm. They are mostly applied in visual recognition and can be used for the...
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Bayesian Convolutional Neural Networks in Medical Imaging Classification: A Promising Solution for Deep Learning Limits in Data Scarcity Scenarios
Deep neural networks (DNNs) have already impacted the field of medicine in data analysis, classification, and image processing. Unfortunately, their...
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Deep-Stacked Convolutional Neural Networks for Brain Abnormality Classification Based on MRI Images
An automated diagnosis system is crucial for hel** radiologists identify brain abnormalities efficiently. The convolutional neural network (CNN)...
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Diagnostic accuracy of automated ACR BI-RADS breast density classification using deep convolutional neural networks
ObjectivesHigh breast density is a well-known risk factor for breast cancer. This study aimed to develop and adapt two (MLO, CC) deep convolutional...
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Current status and prospects of artificial intelligence in breast cancer pathology: convolutional neural networks to prospective Vision Transformers
Breast cancer is the most prevalent cancer among women, and its diagnosis requires the accurate identification and classification of histological...
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Detection of Pacemaker and Identification of MRI-conditional Pacemaker Based on Deep-learning Convolutional Neural Networks to Improve Patient Safety
With the increased availability of magnetic resonance imaging (MRI) and a progressive rise in the frequency of cardiac device implantation, there is...
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Detecting the location of lung cancer on thoracoscopic images using deep convolutional neural networks
ObjectivesThe prevalence of minimally invasive surgeries has increased the need for tumor detection using thoracoscopic images during lung cancer...
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Development and validation of a deep learning model using convolutional neural networks to identify femoral internal fixation device in radiographs
ObjectiveThe purpose of this study is to develop and validate a deep convolutional neural network (DCNN) model to automatically identify the...
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Explainable convolutional neural networks for assessing head and neck cancer histopathology
PurposeAlthough neural networks have shown remarkable performance in medical image analysis, their translation into clinical practice remains...
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Automatic lesion segmentation using atrous convolutional deep neural networks in dermoscopic skin cancer images
BackgroundMelanoma is the most dangerous and aggressive form among skin cancers, exhibiting a high mortality rate worldwide. Biopsy and...
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A primer on deep learning and convolutional neural networks for clinicians
Deep learning is nowadays at the forefront of artificial intelligence. More precisely, the use of convolutional neural networks has drastically...
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Artificial intelligence-driven enhanced skin cancer diagnosis: leveraging convolutional neural networks with discrete wavelet transformation
BackgroundArtificial intelligence (AI) has shown great promise in the field of healthcare as a means of improving the diagnosis of skin cancer. The...
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Convolutional neural networks for the differentiation between benign and malignant renal tumors with a multicenter international computed tomography dataset
ObjectivesTo use convolutional neural networks (CNNs) for the differentiation between benign and malignant renal tumors using contrast-enhanced CT...
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Evolutionary Deep Attention Convolutional Neural Networks for 2D and 3D Medical Image Segmentation
Develo** a convolutional neural network (CNN) for medical image segmentation is a complex task, especially when dealing with the limited number of...
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Localization of contrast-enhanced breast lesions in ultrafast screening MRI using deep convolutional neural networks
ObjectivesTo develop a deep learning–based method for contrast-enhanced breast lesion detection in ultrafast screening MRI.
Materials and methods ...
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Artificial intelligence in tongue diagnosis: classification of tongue lesions and normal tongue images using deep convolutional neural network
ObjectiveThis study aims to classify tongue lesion types using tongue images utilizing Deep Convolutional Neural Networks (DCNNs).
Methods ...
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Advancements in the future of automating micromanipulation techniques in the IVF laboratory using deep convolutional neural networks
PurposeTo determine if deep learning artificial intelligence algorithms can be used to accurately identify key morphologic landmarks on oocytes and...
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Automated fundus ultrasound image classification based on siamese convolutional neural networks with multi-attention
Fundus ultrasound image classification is a critical issue in the medical field. Vitreous opacity (VO) and posterior vitreous detachment (PVD) are...
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Visual ensemble selection of deep convolutional neural networks for 3D segmentation of breast tumors on dynamic contrast enhanced MRI
ObjectivesTo develop a visual ensemble selection of deep convolutional neural networks (CNN) for 3D segmentation of breast tumors using T1-weighted...
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Comparison of convolutional neural networks for classification of vocal fold nodules from high-speed video images
ObjectivesDeep learning is in this study used through convolutional neural networks (CNN) to the determination of vocal fold nodules. Through...
